Data Warehousing Archives

Yashica Chopra
November 03, 2024
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9 Building Blocks of Data Engineering Services – The Fundamentals

Data engineering is the key for businesses to unlock the potential of their data. Here, we’ll discuss the fundamentals aka the building blocks of Data Engineering Services, and the role of data engineering in helping businesses make data-driven decisions in real time. Data engineering services are gaining demand due to digital transformation and the adoption of data-driven models in various business organizations. From startups to large enterprises, businesses in any industry can benefit from investing in data engineering to make decisions based on actionable insights derived by analyzing business data in real-time. Statistics show that the big data market is expected to reach $274.3 billion by 2026. The real-time analytics market is predicted to grow at CAGR (compound annual growth rate) of 23.8% between 2023 and 2028. The data engineering tools market is estimated to touch $89.02 billion by 2027. There’s no denying that data engineering is an essential part of business processes in today’s world and will play a vital role in the future. But what is data engineering? What are the building blocks of data engineering services? How can it help your business achieve your goals and future-proof the process? Let’s find out below. What are Data Engineering Services? Data engineering is the designing, developing, and managing of data systems, architecture, and infrastructure to collect, clean, store, transform, and process large datasets to derive meaningful insights using analytical tools. These insights are shared with employees using data visualization dashboards. Data engineers combine different technologies, tools, apps, and solutions to build, deploy, and maintain the infrastructure. Data engineering services are broadly classified into the following: Azure Data Engineering Microsoft Azure is a cloud solution with a robust ecosystem that offers the required tools, frameworks, applications, and systems to build, maintain, and upgrade the data infrastructure for a business. Data engineers use Azure’s IaaS (Infrastructure as a Service) solutions to offer the required services. Finding a certified Microsoft partner is recommended to get the maximum benefit from Azure data engineering. AWS Data Engineering AWS (Amazon Web Services) is a cloud ecosystem similar to Azure. Owned by Amazon, its IaaS tools and solutions help data engineers set up customized data architecture and streamline the infrastructure to deliver real-time analytical insights and accurate reports to employee dashboards. Hiring certified AWS data engineering services will give you direct access to the extensive applications and technologies in the AWS ecosystem. GCP Data Engineering Google Cloud Platform is the third most popular cloud platform and among the top three cloud service providers in the global market. From infrastructure development to data management, AI, and ML app development, you can use various solutions offered by GCP to migrate your business system to the cloud or build and deploy a fresh IT infrastructure on a public/ private/ hybrid cloud platform. Data Warehousing Data warehousing is an integral part of data engineering. With data warehousing services, you can eliminate the need for various data silos in each department and use a central data repository with updated and high-quality data. Data warehouses can be built on-premises or on remote cloud platforms. These are scalable, flexible, and increase data security. Data warehousing is a continuous process as you need to constantly collect, clean, store, and analyze data. Big Data Big data is a large and diverse collection of unstructured, semi-structured, and structured data that conventional data systems cannot process. Growing businesses and enterprises need to invest in big data engineering and analytics to manage massive volumes of data to detect hidden patterns, identify trends, and derive real-time insights. Advanced big data analytics require the use of artificial intelligence and machine learning models. 9 Building Blocks of Data Engineering Services Data Acquisition Data ingestion or acquisition is one of the initial stages in data engineering. You need to collect data from multiple sources, such as websites, apps, social media, internal departments, IoT devices, streaming services, databases, etc. This data can be structured or unstructured. The collected data is stored until it is further processed using ETL pipelines and transformed to derive analytical insights. Be it Azure, GCP, or AWS Data Engineering, the initial requirements remain the same. ETL Pipeline ETL (Extract, Transform, Load) is the most common pipeline used to automate a three-stage process in data engineering. For example, Azure Architecture Center offers the necessary ETL tools to streamline and automate the process. Data is retrieved in the Extract stage, then standardized in the Transform stage, and finally, saved in a new destination in the Load stage. With Azure Data Engineering, service providers use Azure Data Factory to quickly build ETL and ELT processes. These can be no-code or code-centric. ELT Pipeline ELT (Extract, Load, Transform) pipeline is similar but performs the steps in a slightly different order. The data is loaded to the destination repository and then transformed. In this method, the extracted data is sent to a data warehouse, data lake, or data lakehouse capable of storing varied types of data in large quantities. Then, the data is transformed fully or partially as required. Moreover, the transformation stage can be repeated any number of times to derive real-time analytics. ELT pipelines are more suited for big data analytics. Data Warehouse A data warehouse is a central repository that stores massive amounts of data collected from multiple sources. It is optimized for various functions like reading, querying, and aggregating datasets with structured and unstructured data. While older data warehouses could store data only tables, the modern systems are more flexible, scalable, and can support an array of formats. Data warehousing as a service is where the data engineering company builds a repository on cloud platforms and maintains it on behalf of your business. This frees up internal resources and simplifies data analytics. Data Marts A data mart is a smaller data warehouse (less than 100GB). While it is not a necessary component for startups and small businesses, large enterprises need to set up data marts alongside the central repository. These act as departmental silos but with seamless connectivity

Manpreet Kaur
October 18, 2024
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Artificial Intelligence in Insurance – The Future of Risk Management

The intersection of artificial intelligence in insurance creates a new era of innovation. AI-powered technologies disrupt traditional insurance models, leading to more personalized, efficient, and affordable services. AI is currently being integrated at a soaring rate with Insurance. Around 77% of insurers incorporate AI in their operations this year compared to 61% in 2023. This is the case because AI presents the opportunity to bring radical improvements to the operational model that insurers use to design, price, distribute, and service their products. For example, the current use of robust AI solutions, such as LLMs, is planned by 67% of insurance organizations; the same can be said about future implementation. Opportunities for using generative AI are estimated at $15 billion in the insurance and finance industry by 2025 and at $32 billion by 2027. McKinsey thinks that, at most, Artificial Intelligence could contribute up to $1.1 trillion US dollars to the overall contribution of insurance to the annual global GDP. Insurers are just starting to identify insights and trends as digitalization speeds advance and the amount of data insurers potentially analyze increases. Artificial intelligence insurance companies are not just a trend in insurance but the reality of the business in the foreseeable future. But, why exactly is the topic of Artificial Intelligence in Insurance works? Let’s find out! How Artificial Intelligence in Insurance Works? Digital transformation in insurance is undergoing with the adoption of the Internet of Things (IoT), artificial intelligence (AI), robotics, and other advanced technologies, significantly changing operational methodologies. Here’s the AI is involved in the insurance process: Customer Services Traditionally, the insurance industry relies on human expertise and manual procedures, particularly for processing claims and issuing new policies. The growing use of consumer-connected devices—like cars, fitness trackers, home assistants, and smartphones—generates extensive data, enabling insurers to more accurately assess and understand current and potential customers’ needs. Claims AI in Insurance claims processing reduces time and costs for insurers. By quickly analyzing data and images, AI helps agents with accurate estimates and frees them to focus on more complex tasks. Underwriting and Pricing Automated underwriting solutions with AI help insurers set competitive rates and speed up pricing updates, leading to more personalized and efficient pricing. Sales AI boosts sales by creating new digital channels and integrating with sales tools, simplifying the process for agents and brokers and enhancing customer outreach. Fraud Detection AI insurance in fraud detection enables insurers to analyze vast amounts of data from diverse sources, assess risk factors, and spot anomalies. Advanced algorithms can identify suspicious activities and highlight claims that need further scrutiny, potentially catching issues that might be overlooked in human-only reviews. Risk Prevention AI analyzes historical data and market trends to effectively predict and manage risks. By examining IoT data and past claims, AI provides insights to prevent future issues and tailor risk management strategies. New Products and Channels Insurance products are developed faster with the introduction of AI usage-based products like ‘pay as you drive,’ which change depending on driving behavior and conditions. Moreover, AI enables the delivery of innovative insurance solutions that can be specifically created for an individual based on his/her risk factors. Factors Driving Adoption Of Artificial Intelligence in Insurance AI-driven solutions are boosting insurers’ market share and profitability, driven by several key factors. Advancements in AI and Machine Learning Technologies Rapid progress in AI/ML development services is unlocking new data value. Large language modeling (LLM) allows insurers to streamline AI for insurance claims processing and enhance fraud detection. Generative AI, although in its early stages, combines data, tools, and reasoning to provide valuable insights, promising significant benefits for the insurance sector. Increased Availability of Diverse Data Sources The surge in diverse data sources offers more material for value extraction. Insurers increasingly use third-party data, including consumer credit, marketing information, social media activity, purchasing behavior, criminal records, past claims, and weather data. This supplemental data improves underwriting, risk modeling, claims processing, and marketing practices. Growing Demand for Enhanced Customer Service AI insurance chatbots and virtual assistants enable insurers to deliver prompt, personalized support around the clock. Natural language processing (NLP) powers these front-line solutions, providing continuous basic support and allowing human agents to handle more complex issues. Capabilities of Modern Cloud Data Platforms Modern cloud data platforms like Snowflake offer efficient, cost-effective data storage and processing. Supporting structured, semi-structured, and unstructured data, these platforms allow insurers to use diverse datasets for AI model training and refinement. Scalable compute power ensures effective management of insurance workflow automation. Benefits of Implementing Artificial Intelligence in Insurance Artificial Intelligence in the insurance sector offers benefits like reduced biases and streamlined processes but also faces challenges. It helps personalize coverage and detect fraud, yet lacks transparency and may inadvertently introduce new biases. 1. Reduction of Biases through AI Traditional insurance rates often consider personal factors like credit scores, income, education, occupation, and marital and homeowner status, which can disadvantage low-income buyers despite being unrelated to collision risk. Artificial intelligence insurance models can be trained to exclude these factors, thereby reducing biases. 2. Streamlined Insurance Processes with AI Intelligent automation in insurance enhances efficiency in the insurance sector by swiftly detecting fraudulent claims and expediting the underwriting process, which assesses potential customers’ risk levels. Utilizing historical data, Artificial intelligence insurance models can process new customer information and claims more quickly and cost-effectively than human employees. 3. Flexible Insurance Options Enabled by AI Wearable technology allows insurers to monitor driver behaviors for companies like Uber and Lyft. Safer driving habits can lead to lower premiums, and devices can activate insurance coverage only when drivers are active, reducing costs and providing coverage for service workers who would otherwise need personal policies. 4. Promotion of Safer Driving Habits Artificial intelligence and machine learning in the insurance industry can analyze data from connected devices to identify patterns in driving accidents or mishaps. Insurers can then offer recommendations to companies to reduce the frequency of accidents and costly claims. 5. Lack of Transparency in AI Models AI-based risk models,

Yashica Chopra
October 16, 2024
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Is Azure Infrastructure as a Service The Future of Cloud Computing?

Microsoft Azure is one of the top three cloud computing platforms used by various business organizations. Here, we’ll discuss the basics, use cases, benefits, and examples of Azure infrastructure being the future of cloud computing. Microsoft Azure is a popular cloud platform with an extensive ecosystem of tools, technologies, applications, storages, frameworks, etc., useful for diverse requirements. It is among the top three cloud solutions in the global market. According to statistics, Azure’s market share reached 24% in 2024, and the customer base grew by 14.2% from 2023. Since its launch in 2010, Azure has been a tough competitor. Azure, AWS (Amazon Web Services), and Google Cloud continue to be the top three cloud platforms for SaaS, PaaS, and IaaS solutions. The 2024 Azure Market Report states that Azure has 350,000 customers for cloud computing services. Azure infrastructure as a service (IaaS) can streamline business processes across all verticals and reduce the pressure of maintaining and upgrading the systems on-premises. But what are Azure infrastructure services? Where do data engineering services come into the picture? How can Azure IaaS help a business? Let’s find out in this blog. What is IaaS on Azure? Infrastructure as a service (IaaS) is a cloud computing service where the entire IT infrastructure (storage, networking, backup, applications, virtual machines, etc.) is hosted on a remote cloud server. It allows businesses to save money through the pay-on-demand pricing model. Businesses can reduce the expenses of maintaining the data silos in each department and upgrading the hardware periodically. With IaaS, organizations also gain access to real-time insights and can quickly embrace advanced technologies. Azure infrastructure as a service encourages flexibility, scalability, and reliability of the IT system in an enterprise. From a startup to an established enterprise, any business can invest in Azure IaaS and build a robust cloud-based IT infrastructure. Existing setups can be migrated to the cloud, or a new infrastructure can be built and deployed on the Azure cloud. This depends on various factors like business requirements, timeline, budget, legacy systems, long-term objectives, etc. Testing, implementation, integration, storage, data backup and recovery, web app development, etc., are a part of the services. Since it is a complex process, most organizations prefer collaborating with certified Microsoft Azure partners to handle the task. This ensures complete access to the tools and apps in the Microsoft marketplace and the necessary expertise to keep things running seamlessly. A certified partner has the necessary experience and skills to customize Azure cloud infrastructure to suit the business needs. What is Azure Data Engineering? Data engineering is the process of designing, building, and maintaining data systems to collect, store, and analyze large datasets and derive meaningful real-time insights. It combines many responsibilities and the core part of the data-driven model. Azure data engineering services are provided by certified data engineers who offer end-to-end support in managing data and data systems on the cloud. An Azure data engineer will integrate, transform, and consolidate data from multiple sources to make it possible to derive insights. From building data pipelines to handling structured, semi-structured, and unstructured data in large quantities and helping stakeholders understand the analytical reports, a data engineer has much to do. Data engineering companies also offer Azure IaaS solutions and help businesses build the data warehouse/ data lake on the cloud platform. The experts create the necessary system connections to make the insights accessible to employees through customized dashboards. This helps in making proactive data-driven decisions. Benefits of Azure Infrastructure as a Service (IaaS) Enhanced Data Security and Encryption Azure infrastructure encryption offers built-in security features and capabilities to keep the business data and systems safe from unauthorized access. It also helps organizations adhere to data privacy regulations based on geographical location and industry standards. With Azure, businesses can reduce the risk of cyber threats and protect user data. Centralized and Cloud-Based Infrastructure Maintaining individual IT systems with data scattered throughout the enterprise is not only cost-intensive but also stressful. This reduces data quality and can result in outdated or incorrect insights. With Azure infrastructure as a service, organizations can build a unified and centralized IT infrastructure that anyone in the enterprise can access. It is a simplified and efficient way to run the business processes. Fewer Hardware Maintenance Costs Maintaining legacy systems can be a costly exercise for businesses as they become outdated over the years and will no longer be compatible with new technologies. Organizations have to periodically invest in new hardware and pay for maintenance services to make sure they can access the latest tools in the market and gain a competitive edge. By switching over to Azure infrastructure as a service, most business hardware can be eliminated. Employees access the virtual machines from their devices and can work remotely. Streamlined Operations One of the biggest advantages of data engineering services and IaaS is automation. Instead of wasting time and resources on manually performing repetitive actions, businesses can automate even complex tasks. This reduces the workload on employees and minimizes the risk of human error. Additionally, the workflows are streamlined into an order that maximizes efficiency without compromising quality or control. Remote and Restricted Access Remote working has become a norm in recent times. Employees have to have access to business systems, data, tools, and dashboards irrespective of their location. At the same time, people without authorization (hackers, scammers, etc.) should not be allowed to gain control over the business processes. Azure IaaS balances these two aspects with ease. It encourages remote collaboration between teams but also provides restricted access to confidential data. Standardized Applications Azure infrastructure as a service encourages the standardization of business processes and applications by developing a unified platform to manage all tasks and systems. Furthermore, the third-party apps and tools belong to the Microsoft ecosystem and follow the same standards. This results in improving consistency in performing day-to-day activities and achieving the desired results every time. Flexibility and Scalability Another benefit of Azure infra developer is the flexibility it offers to businesses. The

Yashica Chopra
October 07, 2024
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Data Engineering Services vs Warehousing vs Analytics: Pick Your Data Strategy

With data becoming a crucial part of the global industry, it is essential to unlock its full potential to boost your business. Here, we’ll discuss data engineering services, data warehousing, and data analytics to help determine the best choice. Data is the key to a successful business. Instead of storing the data in outdated setups like silos, you can create a central data repository and allow employees restricted access to the datasets. This makes it easier to use the business data for analytics and insights. Employees at all levels can make data-driven decisions by accessing the insights through their dashboards. Data analytics, data warehousing, and data engineering are different yet interlinked concepts used to streamline data collection, storage, and analysis in an enterprise. Statistics show that the global big data and data engineering market is expected to be $75.55 billion in 2024 and predicted to reach $169.9 billion by 2029 at a CAGR (Compound Annual Growth Rate) of 17.6%. However, you may have questions about which service to use for your business. Should you hire data engineering services, or will it be enough to pay for third-party or embedded data analytics solutions? Where does data warehousing fit into the grand scheme of things? Let’s find out in this blog. Is Data Warehousing the Same as Data Analytics? A data warehouse is a central repository or a large database containing massive amounts of business-related data. It can be built on-premises or on the cloud platform. A data warehouse is connected to several internal and external sources as well as third-party applications like business intelligence tools, data analytical dashboards, etc. Data warehousing services include setting up the repository, building data pipelines, streamlining data flow, maintaining the database, and periodically upgrading the systems. Data analytics is the process of converting raw data into actionable insights to make data-driven decisions. It helps see the hidden patterns, trends, and correlations in historical and present datasets. The insights derived are shared with end-users (employees) via data visualization dashboards. Data analytics help shape business processes to deliver better results while consuming fewer resources. It can be used to understand market trends, customer behavior, product performance, employee productivity, etc., and make the necessary changes to achieve business goals. In short, data warehousing is not the same as data analytics. While the data warehouse is used to store and clean data, analytical tools help to understand what the data means and how it can help empower the business. Creating a synergy between data warehouse and data analytics will certainly give you the best results. So, what is the difference between a Data Warehouse Engineer and a Data Analyst? A data warehouse engineer is responsible for managing the entire development lifecycle of a data warehouse. It is a backend process that includes many activities, such as building the warehouse, system connections, ETL, performance management, resource management, dimensional design, etc. A data warehouse engineer works with data scientists, data analysts, and data engineers to ensure the data flow is smooth and seamless across the enterprise. A data analyst uses the data stored in data warehouses and data lakes to review the information, detect patterns, and identify key insights useful for the business. The primary responsibility of a data analyst is to find solutions for various business problems by analyzing historical and real-time data and sharing insights with decision-makers. The data analyst has to collaborate with data warehouse engineers, software developers, and data scientists to run the data-driven model without interruptions or errors. What are Data Engineering Services? Data engineering encompasses various processes like data collection, data storage, data cleaning, and data analysis for large amounts of raw, structured, unstructured, and semi-structured data. It allows data scientists and data analysts to derive in-depth insights using various statistical and analytical methods. Data engineering also includes ensuring that the quality of the datasets is high to prevent inaccurate insights. Data engineering services cover a broader area and include many responsibilities. For example, it can also include data warehousing solutions or a part of the warehousing processes. Typically, data engineering involves the following activities: So, do data engineers do data warehousing? Yes. Data engineers play a role in designing, developing, and maintaining the data warehouse and its connections. However, note that data warehousing services are only a part of data engineering responsibilities. The top data engineering companies provide end-to-end services, right from planning the strategy to maintaining and upgrading the relevant systems, tools, and processes in your business. Data engineers collaborate with other experts like software developers, data warehouse engineers, data scientists, and data analysts to create a robust data model in the enterprise. Which is Better: Data Analytics or Data Engineering? Despite the overlap in some processes and data being the common factor, there are quite a few differences between data analytics and data engineering services. A business can invest in data analytical tools and derive insights to make important decisions. It can partner with a data analytics company to get embedded analytics through customized dashboards without setting up the IT infrastructure in the enterprise. Data analytics as a service is a cloud-based solution where third-party companies handle most of the backend work and share insights and reports with businesses. Data engineering is much more complex and extensive than data analytics. Data engineering consulting companies build data pipelines, set up system integrations, build data warehouses/ data lakes, connect the necessary data analytics and business intelligence tools, and maintain proper data flow across the IT infrastructure. Programming, database management, and cloud computing are part of the services. In today’s competitive scenario, investing in data engineering services is a better option than limiting your business to data analytics. This empowers you to unlock the full potential of data and gain an edge over competitors. It also keeps you one step ahead and capable of making proactive decisions to grab market opportunities or avoid pitfalls. Data Engineering Services vs. Data Warehousing vs. Data Analytics As you can see, the three aspects are different but interconnected on

Manpreet Kaur
September 24, 2024
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Top Data Warehousing Companies Revealed: 11 Players Shaping the Industry

Data warehousing companies help enterprises capture lots of data from many sources for processing. Among the current excellent market leaders that offer an efficient method of managing data and therefore offering the market a competitive advantage for organizations are Snowflake, DataToBiz, Google BigQuery, and Amazon Redshift. Imagine you are flooded with data from the marketing department, the sales department, the finance department, and even the web analytics department. How would you make any sense of it all? If there were few data sources and the volume of data was small in each, then it is possible to combine them manually. What happens when the amount of data streaming in from all the sources becomes unmanageable, and the volumes are too big? That is where data warehousing services come in handy. This technology consolidates all your information into one point, which allows you to get more complete results from processing all interrelated fields at once, taking into account all the information and basing it on full data. Now, let’s examine what a data warehouse is and learn about the 11 leading data warehousing companies globally. This will help you get an overview of how it could streamline and improve the way you apply information within your organization. What is a Data Warehouse? A data warehouse is a central repository that initially supports and handles tremendous historical data collected from various parts of a company. These systems are designed for digital transformation strategies and enable businesses to define trends and patterns that could thus be of strategic importance in decision-making. This is not to say that data warehouses resemble regular databases; however, the former is more appropriate for analyzing large historical information sets. They are faster than traditional methods, enhance the quality of data, and offer rich information in comparison to other means. This helps businesses in several ways: Analysts estimate that the market for enterprise data warehouses (EDWs) shall grow further; the increase is estimated at $39.23 billion from 2024 to 2028. This growth is a result of the rising volume of data available within the different sectors. The increase in data calls for organizations to adopt state-of-the-art cloud data warehousing toolkits in the market to remain relevant. Top Data Warehousing Companies Shaping The Industry To help you pick the perfect solution for your business, let’s dive into some of the best data warehousing companies around the world: DataToBiz DataToBiz is a data intelligence firm based in India offering data warehousing services to manage, store, and analyze large volumes of data to make informed business decisions. Their team of experts offers advanced services in data warehousing consultation, development, integration, and migration, always prioritizing client needs and delivering tailored solutions. Key Features: Things to Consider: Amazon Redshift Amazon Redshift provides adaptable data warehousing services tailored for the AWS cloud, making it a budget-friendly choice for analyzing extensive datasets kept in S3. It provides a user-friendly interface that’s particularly convenient for those familiar with the AWS environment. Key Features: Things to Consider: Google Cloud Platform Google BigQuery stands out among data warehousing companies with its serverless setup, which eliminates the hassle of managing infrastructure. You only pay for the data processing you use, making it both affordable and efficient. It’s engineered to process extensive datasets rapidly and incorporates machine learning to delve deeply into data analysis. Key Features: Things to Consider: Snowflake Inc. Snowflake provides a cloud-based data warehouse software that scales easily with elastic computing for on-demand processing power. It uniquely separates storage from computing, enhancing cost efficiency. Thanks to Snowflake’s support for SQL queries, users already familiar with SQL will find it straightforward to perform data analysis. Key Features: Things to Consider: Microsoft Azure Azure Synapse Analytics, previously known as Azure Data Warehouse, is a modern cloud-based data warehouse software that works closely with other Azure services. This integration forms a cohesive data environment, simplifying the management of data across different platforms. Key Features: Things to Consider: IBM IBM Db2 Warehouse is a dependable and secure data warehousing platform tailored for seamless integration with the wider IBM analytics ecosystem. It’s built to support demanding data workloads with its scalable design and top-tier performance capabilities. Key Features: Things to Consider: Oracle Oracle Autonomous Data Warehouse provides a highly automated data warehousing platform hosted on Oracle Cloud. This platform utilizes machine learning to optimize workloads and efficiently allocate resources, ensuring seamless integration with Oracle’s suite of services. This advanced, self-managing architecture reduces the complexity of data warehouse product operations for businesses leveraging Oracle Cloud. Key Features: Things to Consider: Teradata Teradata stands out among data warehousing companies as a high-performance solution designed for crucial applications. It’s particularly known for its solid security measures, ensuring data safety and adherence to compliance standards—ideal for businesses handling sensitive information. Key Features: Things to Consider: SAP SAP HANA is particularly a data warehouse product that is designed to process data in-memory which makes it much easier for it to provide data as well as analysis faster. This makes it a perfect asset for organizations that require real-time access to information for quick decision-making. Key Features: Things to Consider: Cloudera Among the data warehousing companies, Cloudera has been noted as the best because it provides an open-source data platform capable of customization appropriately for the needs of its clients. It supports virtually any type of data format and data source and while it is relatively easy to use it does require some degree of technical skill to install and maintain. Key Features: Things to Consider: Firebolt Firebolt is a cutting-edge cloud data warehouse that’s highly favored by engineers due to its remarkable speed. It’s crafted to deliver an exceptional user experience through its intelligent storage solutions and efficient query handling. For those familiar with SQL, Firebolt will feel like second nature as it adheres to standard SQL protocols. Key Features: Things to Consider: Conclusion The applicability of the data warehousing system is most appropriate for companies that are interested in going deeper than just filtering information for

Manpreet Kaur
September 12, 2024
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Top 10 Reasons to Prioritize Data Warehousing Services

Data warehousing is like building an organized library for datasets in your organization. It integrates data from various sources, improves data quality, and makes it easy to analyze and make smart decisions upon it. Investing in data warehousing services ensures you can make well-informed decisions that keep you ahead of the competition. As decision-making moves towards relying on data and analytics, the demand for data solutions has increased. Incidentally, 31% of respondents are eager to quickly scale up their analytics spend to support more users and handle bigger data. Additionally, 28% are aiming to focus on improvement in the infrastructure which would support the cloud, on-premise as well and regional computing. But how can you efficiently handle petabytes of data? The answer is the data warehouse. Data warehouses are a kind of central repositories designed for storing and processing huge quantities of information from various segments of an organization. When approaching the question of investing money in data warehousing services, you should be acquainted with what gets you the maximal profit. In this blog, let’s try to break down what is important to know but might be too obvious to think of when getting started with data warehousing services. But first, let’s start with the basics. What is a Data Warehouse? A data warehouse, often called an enterprise data warehouse (EDW), is a central system where businesses keep important information like customer and sales data. This information is stored for analysis and reporting. Data warehouses are essential for generating insights and supporting decision-making through business intelligence (BI). They typically hold both current and historical data that has been collected, transformed, and loaded (ETL) from various sources, including internal and external databases. Essentially, a data warehouse serves as a business’s single source of truth (SSOT) by consolidating data into a stable, standardized system that relevant employees can easily access. These systems are designed for online analytical processing (OLAP) and enable fast, efficient analysis of data from multiple angles. Data warehouses can store vast amounts of summarized data, sometimes reaching several petabytes. Why Should You Have a Data Warehouse? The main benefit of a data warehouse is that it brings together data from various sources into a unified format. This consistency ensures that the data is accurate, leading to well-informed decisions. When data is standardized across the business, every department can generate consistent and reliable results. Traditionally, data warehouses were located on servers within a company’s premises (on-premise). However, nowadays, many data warehouses have moved to the cloud, where they can store and analyze vast data sets. Some popular cloud-based data warehousing platforms include: Data warehousing services often serve as a single, reliable source of truth for businesses, centralizing big data in a secure, stable, and standardized system that’s accessible to the right teams and employees. Reasons To Invest in Data Warehousing Services It is important for an organization that requires information in real time to make the right decisions to use a data warehouse. Still, these reasons can help you determine whether, in your case, data warehousing services are worth the investment. 1. Unlock Data-Driven Potential Decisions are no longer hove on guesswork or instinct – at least they shouldn’t be. Indeed the modern-day leader has the great fortune of having a basis for decision making informed by current data and this is made possible through a data warehouse. For information power to be realized optimally, there must be a loss of data ownership where one department usually owns most of the data. A data warehouse can rectify this scenario, and those in need of some specific details do not need to go through all sorts of hoops or even other departments. When it is set up as a single source organizing all other material, data warehousing services ensure that information seekers are well-equipped to find what they require and use it decisively to chart the destiny of the organization. 2. Harness the Power of Automation Data warehousing services open up opportunities for businesses to explore automation. Automating different parts of operations is gaining traction, especially as people see how it can help avoid costly errors and speed up processes. Market studies suggest that the global industrial automation market could reach $265 billion by 2025, a significant jump from $175 billion in 2020. Data warehouses play a crucial role in supporting these automation efforts. Businesses can use software-driven workflows to automate tasks like data access and transfer, reducing the time needed to gather information for auditors, investors, or other stakeholders. Automation can also speed up data analysis, helping uncover insights much faster. Additionally, it’s possible to automate error detection and logging, making it easier to spot potential issues and address them quickly. By understanding how data warehousing services are used within an organization, businesses can identify the best areas to implement automation. 3. Keep Your Data Secure When data is scattered across multiple locations, makes your security more challenging. Many leaders don’t even know how much data they have or where it’s stored. With data warehousing services, everything is stored in one place, making it easier to track and secure information. Plus, most data warehousing platforms come with built-in security features. Some can block harmful SQL code from outside attacks, while others limit how much data someone can view at once, reducing the risk of unauthorized use. Organizations can also control who accesses the data warehouse and why. This ensures that people only see what’s relevant to their job. Additionally, some data warehouses lock out users who try to log in from unusual locations, making it harder for hackers to take advantage. To effectively implement data warehousing services in your company, it is advisable to leverage digital transformation consulting services for a well-guided and strategic implementation. 4. Enhance Data Quality and Consistency Sustaining high quality and consistency of the data is an essential requirement for your organization. Worse the cloud data is unstructured or inaccurate and hence will not be of much use. A data warehousing system can aid in

Manpreet Kaur
February 28, 2024
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How to setup a data warehouse for manufacturing data?

Data warehouses store data and facilitate quick analysis and reporting for actionable insights. With effective data warehouses, you can gather data from different data sources. In this blog, we’ll find out how manufacturing analytics companies can build a data warehouse for manufacturing data and gain relevant insights. Manufacturing organizations are undergoing transformational changes owing to the exponential growth of data. According to the IDC forecast, the global data sphere is expected to expand by 175 zettabytes by 2025. This massive growth indicates a data-driven world characterized by constant tracking and monitoring. Data plays an important role in highlighting areas for improvement, whether it’s inventory management, production, logistics, and warehouses. The challenge lies in collecting data in real-time and using it efficiently. By leveraging a data warehouse for manufacturing data, companies can store and process vast amounts of data with the help of manufacturing analytics solutions. What is data warehouse for manufacturing ? A data warehouse for a manufacturing company is a digital repository of disparate data sets. It gives a consolidated view of data from different systems such as operational and transactional data management. Manufacturing organizations collect information across different stages of their processes, such as product and process design, assembly, maintenance, and recycling. A data warehouse aggregates structured data from multiple sources, giving accurate data analysis. How to create a data warehouse setup? Here is a quick overview of the steps of building a data warehouse for manufacturing companies Step 1: Understand business requirements Note down the functional and non-functional requirements of your business according to their priority. For example, if your business will expand and grow in the immediate future, scalability must be your top priority. Figure out departmental goals and align them with the project. Assess the existing tech stack and data to get an idea of the current and future needs. Step 2. Investigate source data Define all the data sources and identify the primary sources of record to prevent unnecessary data loading, since specific datasets might be present across multiple storage systems. For example, you can transfer the sales order information from the order management system to logistics software. However, the OMS serves as the single data source, since the logistics software may alter data, compromising the quality of insight. Step 3. Develop conceptual, logical, and physical data models Once you have delineated all the business requirements, you need to create a preliminary enterprise data warehouse model to visualize and represent key business processes and their interrelationships. Make sure you build these models in collaboration with the domain experts to account for industry-specific subtleties. Conceptual data models help to set up relationships among core business entities and outline the information needs of an organization. For instance, a supply chain company might identify entities such as products, customers, shippers, carriers, suppliers, orders, and manufacturers. Logical data models have more elaborate details such as attributes (columns) associated with each business entity. For example, the product’s price Physical data models include primary and foreign keys. A primary key works as a unique identifier within a table, while a foreign key is inserted from one table to another to establish a relationship between tables. Since business operations evolve continuously, it’s imperative to ensure data models remain adaptable. Step 4. Define and create a data warehouse schema Now you need to structure the final version of data modeling into a data warehousing schema. Select the most suitable schema from different schema types, consulting a software architect. Step 5. Deploy a data warehouse architecture gradually When you have a data warehouse schema in place, create a data warehouse architecture. Focus on factors such as cost, security, performance, and scalability to choose a flexible architecture according to business requirements. What are the applications of data warehouses in manufacturing? Manufacturing production and distribution organizations centralize their data using a data warehouse, giving a comprehensive analysis to determine existing patterns and trends, forecast market shifts, pinpoint growth opportunities, identify areas for development, and facilitate strategic decision-making. They face critical decisions regarding in-house production and outsourcing that impact the industry. By using OLAP (Online Analytical Processing) tools within data warehouses, businesses can analyze trends, detect early indicators of potential challenges, and enhance decision-making. Data warehouses monitor product shipments and portfolios, allowing companies to identify product lines and evaluate underperforming ones depending on customer feedback and historical performance metrics. Characteristics of a Data Warehouse The main characteristics of data warehousing in the manufacturing industry typically include: Subject-oriented In a data warehouse, decision-makers (stakeholders, executives, and leaders) analyze data by focusing on specific subject areas, by narrowing relevant data sets. This ensures a clear understanding and streamlined analysis by limiting unnecessary information. Data warehouses are organized on specific subject areas such as customer data and inventory to facilitate analysis. Integrated Data warehouses from disparate sources within an organization are consolidated and standardized in a data warehouse to ensure consistency and coherence across complete datasets. Time-variant Data warehouses store historical data over time, including a temporal element and spanning an extensive time horizon. The immutability of time elements is a crucial aspect of time variance and record key displays time variance. Non-volatile Once data is uploaded in the data warehouse, data is updated to protect it from temporary changes. The data is in read-only form and allows only access and loading functions. What are the four phases of data warehouse design? Manufacturing analytics companies implement the below phases to design data warehouses to ensure the effectiveness of infrastructure. Offline operational database: In this first stage, data is transferred from operating systems to servers. This separation prevents any impact on the performance of the OS, enabling easy data loading, processing, and reporting. Offline data warehouse: During this stage, data is updated periodically since the data is refreshed from the operational database. Real-time data warehouse: At this stage, data warehouses are updated in real-time as transactions occur in the operational database. It involves event-based triggers that send notifications to update records accordingly. Integrated data warehouse: All the transactions are updated

Yashica Chopra
February 13, 2024
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Transforming Hotel Data Analytics : Resilient Data Warehouse

The travel and hospitality industry is evolving through the adoption of data analytics and BI solutions. This is done by modernizing the hotel data analytics infrastructure. Here, we’ll discuss the ways to build a secure and scalable data warehouse and the role of analytics in the industry. The hospitality industry is investing in data analytics and business intelligence to effectively manage the increasing demand from customers. Data analytics helps unlock the hidden trends and patterns in large amounts of data to understand customer behavior, preferences, likes, dislikes, etc. It enables hotels and similar service providers to streamline operations and personalize offerings based on customer requirements. The global hospitality market is valued at $3.95 million in 2024 and is expected to touch $7.239 million by 2027 at a CAGR (compound annual growth rate) of 10.62%. Hotels can adopt data-driven models to derive real-time hospitality business intelligence insights and reports to make faster and better decisions. For this, you need to invest in a reliable, secure, and scalable data warehouse. In this blog, we’ll read more about the role of data analytics in the industry and the best practices to follow when building a data warehouse for hotel data analytics. What is Hospitality Data Analysis? Hospitality data analytics is the use of analytical tools to process historical and real-time data from the hospitality industry. It is a powerful tool that can positively impact various aspects of the business, such as customer experience, marketing, pricing, food and beverage sales, occupancy rates, etc. Hotels prefer to partner with a reliable hospitality data analytics company to set up the necessary IT infrastructure for implementing data analytics and business intelligence. The service provider will build a data warehouse and integrate it with BI tools like Tableau, Power BI, etc., and create custom dashboards for employees to access the insights in real time. Best Practices for Building a Secure and Scalable Data Warehouse The global active data warehousing market touched $10.8 billion in 2023 and is expected to reach $21.5 billion by 2032 at a CAGR (compound annual growth rate) of 7.68%. The data warehouse is a central repository storing massive amounts of data collected from multiple sources. It can be integrated with numerous third-party applications to run real-time analytics and derive business intelligence reports. However, building a data warehouse requires planning and expertise. You should ensure it is secure, scalable, and built using the best tools and technologies in the market. Typically, a data warehouse contains three main components Each layer is equally important and has definite purposes. These have to be aligned with your business requirements and long-term goals. The data warehouse is not limited to the present but is a tool for the future. That’s why data warehouse developers and service providers follow the below-listed best practices to deliver the best travel analytics solutions to businesses. Choosing the Technology A data warehouse can be built using various databases like traditional relational databases, open-source solutions, cloud-based databases, columnar databases, etc. Here, you choose a type based on your business volume and future plans. Since it gets expensive to build multiple data warehouses, you need a model that can be easily scaled and expanded as your business grows. That way, you add more layers to the existing model without starting from scratch every time. Moreover, the data warehouse should continue to deliver efficient results without lags and breakdowns. Open-source and cloud-based data warehousing models are preferred in today’s world due to the flexibility and customizability they offer. Designing the Data Warehouse Model The data modeling method you select affects the analytics and insights you derive by processing the datasets in the data warehouse. Go for reliable data modeling techniques like snowflake schema or start schema as they allow optimization of data retrieval. This leads to efficient query processing without consuming too many resources. You should also consider the types of queries you will use during day-to-day work. For example, a hotel employee has to constantly track the number of guests, advance booking, available free rooms, etc. Streamlining the ETL Process ETL stands for Extract, Transform, and Load. This stage focuses on extracting data from multiple sources and transforming it into structured formats to eliminate redundancy and then loading it into the data storage systems. With the continuous generation of data in the hospitality industry, the derived hotel data analytics can be accurate when the ETL process is efficient and free of errors. Techniques like parallel processing, data validation, etc., can enhance the ETL pipeline and create seamless data flow in the establishment. Ensuring Data Integrity and Consistency Data is the core of data-driven decision-making models. Insights derived from low-quality data can be unreliable and incorrect, leading to wrong decisions. This can be very costly, especially in the hospitality industry, where customer experience is a priority. For a hotel to derive accurate and actionable insights, the input data used for hotel data analytics has to be of top quality and free of mistakes and duplication. The data warehouse should have the means to implement data checks at various stages to increase overall consistency and quality. Data profiling techniques have to be implemented to detect anomalies in data sets, tags, etc., and highlight missing or incorrect values before the data is used for business intelligence reporting. Focusing on Scalability and Performance With new data being created every minute, you should inevitably prepare to scale the data warehouse periodically. There are different ways to scale, such as horizontal scaling, vertical scaling, data compression, indexing, partitioning, etc., that allow the central database to accommodate more data for storage and analytics. At the same time, weighing down the data warehouse with massive amounts of datasets can result in lags and delays. This has to be countered to ensure the efficient performance of the data analytics tools. Hospitality data analytics company like DataToBiz helps businesses find the best solutions to ensure scalability and performance in the present and future. The Need for Data Backup and Recovery Planning Data backup is a must for every business. Data loss is one of the biggest concerns and

Pawan Chabra
January 23, 2024
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Top 7 Data Warehousing Consultants for US Travel & Tourism 2024

In 2024, data-driven decision-making is driving the travel and tourism industry. Data warehousing consulting companies offer personalized experiences and streamline operations. Here are the leading data warehousing consulting companies for US travel and tourism. The travel and tourism industry stands at the crossroads of innovation with data-driven decision-making being the critical driver. As destinations strive to offer personalized experiences and streamline operations, the role of data warehousing for tourism has emerged as a linchpin for success. With close to 70% of all travel bookings being made online, it is the right time for the industry to move towards building a positive experience for the long term. The industry relies on several data sources, from customer preferences and booking patterns to operational logistics and market trends. A comprehensive data warehousing strategy becomes the backbone, seamlessly integrating, managing, and analyzing this wealth of information. This not only enhances operational efficiency but also empowers businesses to anticipate and meet the dynamic demands of today’s travelers. Yet, the implementation of effective data warehousing solutions isn’t a standalone endeavor; it necessitates a strategic partnership with data warehousing consulting companies. Beyond the intricacies of technology, consulting services bring invaluable expertise to the table, ensuring that data warehousing aligns seamlessly with organizational goals. 7 Top Data Warehousing Consulting Companies for US Travel & Tourism in 2024 Selecting the right data warehousing consulting partner sets the right foundation for a travel and tourism business. Here are the 7 top data warehousing consulting companies for US travel & tourism in 2024: DataToBiz DataToBiz has established itself as a go-to partner for organizations seeking comprehensive and cutting-edge travel analytics and managed analytics solutions. With a robust suite of services, the company excels in providing expert guidance through its data warehouse consulting, helping clients navigate the complexities of design and infrastructure analysis. Managed analytics professionals at DataToBiz not only recommend optimal alternatives, such as cloud or hybrid data warehouses but also craft robust data integration strategies for managed analytics for the travel industry. Going beyond consultation, the company demonstrates prowess in the development and implementation of data warehouse solutions, tailoring them to meet specific organizational needs within defined timeframes and budgets enabling data-driven decision-making in the tourism and travel sector. Specializing in seamless data migration strategies, DataToBiz envisions a future where data resides in the cloud, offering dedicated support services to ensure ongoing functionality and performance monitoring. With a commitment to holistic data solutions, DataToBiz emerges as a trusted partner, transforming data into a strategic asset for sustainable business growth. Capgemini Capgemini stands as a renowned leader in data management, specializing in providing tailored data warehousing services for the travel and tourism industry. Recognized for their expertise, Capgemini excels in seamlessly integrating disparate data sources, constructing resilient data lakes, and crafting sophisticated data-driven strategies. With a commitment to excellence, they offer comprehensive solutions that empower businesses in the travel and tourism sector to harness the full potential of their data for informed decision-making and operational efficiency. Infosys Infosys, a prominent IT services provider, stands out as a premier data warehousing company with substantial expertise in the US travel and tourism sector. Renowned for its capabilities, Infosys excels in implementing cutting-edge data platforms, automating processes, and upholding robust data governance standards. With a proven track record, Infosys empowers organizations in the travel and tourism industry to leverage advanced data warehousing solutions, fostering efficiency, reliability, and strategic decision-making. Cognizant Ranked among the top data warehousing consulting companies for US Travel & Tourism in 2024, Cognizant brings a robust emphasis on data analytics and digital transformation to the table. Leveraging their expertise, they assist travel and tourism companies in constructing modern data warehouses, extracting actionable insights crucial for informed decision-making. Cognizant’s proficiency extends to providing cloud-based solutions, capitalizing on their industry-specific knowledge to deliver valuable assets for businesses navigating the dynamic landscape of travel and tourism data management. With a focus on innovation, Cognizant stands as a strategic partner for organizations seeking cutting-edge solutions in data warehousing. Mu Sigma Mu Sigma, a global leader in data science and analytics, emerges as a key player in delivering innovative data warehousing solutions tailored to the unique needs of the travel and tourism industry. Renowned for its expertise in predictive analytics and machine learning, Mu Sigma empowers businesses to optimize pricing strategies, personalize offers, and enhance customer engagement. As the world’s largest pure-play Big Data analytics and decision science company, Mu Sigma collaborates with over 140 Fortune 500 companies, amplifying productivity and providing meaningful solutions by integrating people, processes, and platforms. ThoughtSpot ThoughtSpot emerges as a rising star in the data analytics arena, providing a distinctive search and navigation platform designed to simplify data exploration and analysis for business users, eliminating the need for technical expertise. In the context of the travel and tourism industry, ThoughtSpot’s innovative solution proves especially valuable. It facilitates the democratization of data access, empowering teams within travel and tourism companies to make informed, data-driven decisions. ThoughtSpot’s commitment to travel revenue management analytics aligns with the growing demand for intuitive tools, making it a compelling choice for organizations seeking to enhance their analytical capabilities in the dynamic landscape of the travel and tourism sector. IBM As a technological behemoth with a storied legacy in data management, IBM stands out as a powerhouse providing robust data warehousing solutions, including Db2 and Cloud Pak for Data. IBM’s unparalleled expertise in AI and analytics proves instrumental in enabling companies to achieve a profound understanding of their customers. By leveraging IBM’s cutting-edge solutions, businesses in the travel and tourism sector can unlock the potential for personalized experiences, enhancing customer engagement and satisfaction. IBM’s commitment to innovation continues to make it a strategic partner for organizations seeking to harness the power of advanced data warehousing in their operations. Industry Trends: Data Warehousing for Travel and Tourism Industry The travel and tourism industry is undergoing a data revolution, fueled by the explosion of digital bookings, mobile apps, and social media interactions. This has generated a treasure trove of

Kavika Roy
January 22, 2024
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eCommerce Analytics Simplified- Data Warehousing Challenges Solved by Managed Analytics

Data teams face different challenges while storing and analyzing intricate datasets in data warehouses. Find out how you can overcome these challenges by using eCommerce analytics offered by digital commerce managed services tools. Data and analytics play an important role in running an eCommerce business successfully. The analytics tools enable businesses to track and analyze business performance through reports and dashboards, giving meaningful insights. However, when dealing with extensive datasets, businesses face challenges related to storage and analysis. Data warehouses store and manage data to ensure that analytics tools can easily process it to extract meaningful conclusions by analyzing query results. In this blog, we’ll talk about the common challenges in data warehousing and how you can overcome them using managed analytics. What is data warehousing in eCommerce? In the context of eCommerce, data warehousing refers to collecting, storing, and organizing data sets from multiple datasets, enabling businesses to get insights and facilitate data-driven decision-making in eCommerce. Let’s have a quick look at the benefits of data warehousing in eCommerce. What are the common challenges of data warehousing? Data Quality: Errors and improper updates lead to inaccurate data which impacts data quality. As businesses are increasingly implementing digital commerce solutions, they may face the problem of unintentional data silos. This makes data integration difficult throughout the system. Data Accuracy: Inconsistencies may lead to inaccurate data in data warehouses. This further compromises the reliability of insights and reports generated from inappropriate data. Performance: Slow performance of data warehouses results in sluggish query speeds which makes it difficult for the users to make quick decisions. Advanced solutions for data warehousing in eCommerce optimize processes and enhance performance. eCommerce Data Analytics- simplified! eCommerce data analytics includes analyzing large data sets to understand market trends, and customer preferences, and obtain meaningful insights to find out what’s working and what’s not in your eCommerce business. Understanding eCommerce sales analysis allows business owners to make strategic decisions, bringing high productivity and profitability. It helps them to understand the patterns in customer behavior analytics in eCommerce, and their preferences to personalize marketing strategies that resonate with target demographics. Further, it makes it easy to adjust pricing depending on factors such as competition and demand. By using data analytics, businesses can send customized product recommendations to shoppers to increase customer engagement and sales. Also, it predicts future trends to tailor the marketing strategies to yield high ROI. What are the potential challenges in using data analytics on eCommerce platforms? Now let’s discuss the biggest challenges that eCommerce marketers face when dealing with data. Excessive Dependence on Vanity Metrics: Metrics such as page views or social media followers often look fascinating, however, do not correlate with customer engagement or conversions. Businesses should focus on high-priority metrics (cart abandonment, customer lifetime value, and conversion rates) to analyze the growth of their eCommerce business. Ignoring Updates to Data Sources: eCommerce solution providers must update their data sources in real-time to ensure the accuracy and integrity of data. Data Silos: Data silos result in a fragmented view of business performance. Therefore, it is essential to integrate data sets to give a comprehensive view of the business, facilitating prompt decision-making considering all aspects of business. Wrong Interpretation of Data: It is easy to misinterpret data when the context is lacking. For example, a quick increase in website visitors may initially appear positive, but it could be due to a controversial ad or the virality of bad reviews floating around the Internet. Thus, the analytics team must understand and cross-reference data to ensure accuracy. Managed Analytics Tools Now let’s find out some of the top tools managed analytics tech stack include: Data Storage: Amazon Athena, Azure Synapse Analytics, Amazon Redshift, Azure Data Lake Storage, SAP, Amazon S3, MongoDB, MySQL, Azure Integration Services, Hadoop, Google Big Query, and Microsoft SQL Server Data Integration: Microsoft SQL Server, AWS Glue, Python, Apache Airflow, Talend, and Azure Data Factory Business Intelligence: Tableau, Microsoft SQL Server, Power BI, Metabase, MicroStrategy, Excel, Qlik Sense, and Redash Data Ingestion: Kafka, Amazon Kinesis, and Microsoft Azure Data Processing: Microsoft SQL Server, Apache Spark, and Databricks ERP Systems: Oracle Enterprise Resource Planning Cloud, Microsoft Dynamics 365, and SAP S/4 HANA Cloud Partners: Azure, AWS, and Google Cloud Platform How can Managed Analytics Services Simplify Analytics of Enterprise eCommerce Solutions? Managed Analytics are indispensable for eCommerce analytics solution companies. They transform raw data into practical insights ready for implementation. With the assistance of analytics experts, you can obtain the most out of existing business data, and make precise decisions with the help of data-driven tactics. How do Managed Analytics work? The key steps of managed analytics include: Data Collection: Collects data from diverse sources such as databases, files, or APIs. Storage and Cleaning: Stores collected data in a centralized system such as a data warehouse to ensure accuracy and consistency. Analysis and Modelling: Uses advanced analytics tools and algorithms to examine patterns and trends and generate insights to make informed decisions. Visualization and Reporting: Presents insights in understandable formats including visuals, dashboards, and automated reports, making it easy to comprehend information and take necessary action. Continuous Improvement: Monitors the performance of the organization, optimizes processes, and enhances efficiency through regular improvements. How do Managed Analytics Services help Manage eCommerce Analytics? Data Governance: Data governance sets the guidelines and standards to handle eCommerce data to ensure its reliability, accuracy, security, and consistency. It safeguards eCommerce data such as customer information, transaction records, and confidential information to maintain privacy. Big Data Implementation: With Big Data, you can easily manage large amounts of data generated in eCommerce including customer interactions, transactions, and much more. It also tracks patterns and trends to offer predictive analysis. Data Architecture: A robust data architecture organizes and structures eCommerce data, optimizing data flow. It also makes it easy to integrate data from different sources and access it. Data Lake: Data lakes serve as central repositories to store structured and unstructured eCommerce data, making it easy to store, process, and analyze all

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