data engineering Archives - Page 2 of 2

Pawan Chabra
October 23, 2023
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15 Trusted IT Staff Augmentation Service Providers to Consider in 2024

As we step into 2024, the landscape of staff augmentation services and IT staff augmentation service providers is evolving. Businesses tend to outsource IT experts temporarily to fulfill their data engineering needs. This brings us to a list of the top IT staff augmentation service providers, delivering robust talent solutions to elevate your workforce and meet business demands with excellence. A trusted staff augmentation service provider is a strategic partner that assists businesses in acquiring skilled IT professionals on a temporary or project-specific basis, enabling companies to rapidly address skill gaps, scale their teams up or down as needed, and ensure the successful execution of IT projects while often offering cost savings compared to traditional hiring methods. In 2024, when considering staff augmentation service providers for your IT needs, it’s crucial to focus on companies that offer a dynamic blend of expertise, scalability, and adaptability. A report by PR Newswire reveals that companies around the world will spend USD 81.87 billion on staff augmentation. Therefore, leading IT staff augmentation service providers should excel in sourcing top-notch talent and demonstrate a deep understanding of emerging technologies. Their ability to align their talent pool with your specific project requirements and organizational goals while staying ahead of evolving industry trends will be paramount. Why Choose IT Staff Augmentation Service Providers? Choosing IT staff augmentation service providers can be a strategic decision for businesses looking to access specialized skills and expand their workforce without the hassle of traditional hiring. These providers offer flexibility, scalability, and a vast pool of skilled professionals to address specific project needs. Top IT Staff Augmentation Service Providers to Consider in 2024 Companies are constantly seeking innovative ways to stay competitive and agile. Staff augmentation has emerged as a powerful solution to address fluctuating workloads, acquire specialized skills, and maintain flexibility in a dynamic environment. As we approach 2024, the need for reliable staff augmentation services is more critical than ever. To help you navigate this ever-expanding industry, we have compiled a list of trusted staff augmentation service providers to consider in 2024. DataToBiz DataToBiz is a technology company focused on leveraging data to drive business insights and transformation. With a commitment to harnessing the power of data, Datatobiz offers a range of data analytics and intelligence services and consultations. Alongside, DataToBiz is a leading IT staff augmentation service provider specializing in data engineering services. Their core mission is to equip businesses with the precise talent required to thrive in an increasingly data-centric world. DataToBiz offers a pool of certified Data Analysts, Power BI/Tableau Developers, AI/ML Engineers, Azure experts, GCP experts, and more, to deliver excellence to your IT projects. Their team of seasoned professionals is dedicated to providing clients with the expertise they need to harness the full potential of their data resources. Average Rating Review: 4.8(Source- Clutch, Google Business) Toptal Toptal has earned its reputation as one of the premier platforms for connecting businesses with top-tier talent. With a rigorous vetting process, Toptal ensures that clients have access to a curated pool of freelancers and experts in fields such as software development, design, and project management. Their client base spans a variety of industries, from technology to finance, and they operate internationally, serving companies worldwide. Toptal’s reputation is built on a foundation of trust, quality, and excellence. They continue to serve as a beacon for businesses seeking top-notch talent and expertise, demonstrating their commitment to delivering exceptional results to clients worldwide. Average Rating Review: 4.9(Source- Clutch, Google Business) Turing Turing, with their AI-powered deep-vetting talent platform, specializes in matching companies with the engineering talent required for success. Whether it’s staff augmentation or fully managed services, Turing simplifies the process of transforming businesses. The company stands out for its innovative approach to staff augmentation and remote staffing, making it easier for businesses to access top-tier software developers and professionals globally. Their algorithm ensures the perfect match for your project. Average Rating Review: 5.0(Source- Clutch, Google Business) Aimprosoft Since 2005, Aimprosoft has been unlocking its extensive tech talent pool, dedicated to serving enterprises. Their expertise spans platforms like Liferay, Alfresco, and Hybris, alongside web and mobile solutions tailored to meet clients’ requirements. Aimprosoft’s enterprise partners value their comprehensive offerings, which include software development, customization services, expert consulting, and top-tier quality assurance. The staff augmentation services of the company help global companies realize their technology vision. Average Rating Review: 4.7(Source- Clutch, Google Business) ELEKS ELEKS, a trusted partner for global enterprises, SMEs, and tech innovators, empowers businesses through tailored software development, product design, QA, and consultancy services. With a team of over 2000 software experts, ELEKS customizes core services to meet precise business needs. It drives digital transformations, facilitating agility and innovation with technologies like blockchain, Data Science, and Cloud. ELEKS excels in unifying digital ecosystems, and optimizing IT systems, sites, and building remote teams with staff augmentation services for continued growth and future scalability. Average Rating Review: 4.8(Source- Clutch, Google Business) Intellias Intellias delivers comprehensive digital consulting services designed to address specific business challenges encountered by companies of all sizes and across diverse industries during their digital transformation journey. Their team of digital consulting experts from various domains equips clients with the necessary tools and a detailed roadmap to transform bold concepts into tangible business outcomes, enhancing profitability, efficiency, and market demand. Intellias formulates a tailored strategy, ushering clients through solution discovery, technology advisory, quality control, and security assessment, efficiently managing emerging challenges. Average Rating Review: 4.9(Source- Clutch, Google Business) Intelliswift Intelliswift has evolved from a staffing company into a dynamic organization deeply committed to embracing innovation. Their expertise extends to constructing data-rich modern platforms and sophisticated data management and analytics systems, catering to forward-thinking enterprises. Guided by a team of passionate and technocentric innovators, Intelliswift is a strong name in the staff augmentation industry. With over 20 years of staffing experience, they source top talents to deliver breakthrough solutions to industries such as Pharma, BFSI, and Retail. Their customer-centric approach has earned them the trust of over 10 Fortune 100 and

Yashica Chopra
September 05, 2023
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Top 13 Data Engineering Trends and Predictions 2025

Data engineering is the process of building, deploying, and integrating data pipelines to streamline data flow within an enterprise. It is the foundation for business intelligence processes to run and deliver actionable insights. Here, we’ll discuss the top data engineering trends and predictions for 2025. Data engineering is a growing discipline in the global market. It involves the process of designing and building data pipelines to collect, transform, and transport data to end users (data analysts and data scientists) to derive actionable insights. The data pipelines have to connect all data sources to the central data warehouse or data lake. The success and accuracy of data analytics depend on how well data engineers set up the foundation. This requires high-level data literacy skills. Unfortunately, there is a gap between the demand and supply of qualified and experienced data engineers in the market. It’s one of the primary reasons many SMBs and large enterprises partner with offshore data engineering companies to adopt advanced data-driven technologies and processes for effective decision-making. Many experts feel that 2025 will be a vital year for data engineering. In this blog, we’ll take a detailed look at the various big data engineering trends and predictions that will transform the industry at different levels. 13 Top Data Engineering Trends and Predictions in 2025 1. Increase in Cloud Management Cloud has become a favorite for many businesses around the world. Small, medium, and multinational companies are moving their data and IT infrastructure from on-premises to cloud servers. Data Engineering in AWS (Amazon Warehouse Services), Microsoft Azure, Red Hat, etc., are in high demand. While some companies are directly building data pipelines on the cloud, others are migrating their existing systems to cloud servers. 2. Greater Budget Allocation for FinOps Another trend is the need for data cloud cost optimization. Top vendors like BigQuery and Snowflake are already talking about ways to optimize the data cloud cost and make cloud services more cost-effective to businesses from various industries and markets. Financial managers are becoming a part of data teams to ensure that their data engineering strategies and processes will deliver the necessary returns. While there aren’t enough best practices in the industry (data engineering is still in its early stages), data teams are finding ways to overcome the challenges and make their cloud-based data architecture more agile, flexible, scalable, and future-proof. The cost of ownership is also a crucial topic of discussion. 3. Usage-Based Data Workload Segmentation In the current scenario, companies are focusing on using a unified cloud-based data warehouse. For example, AWS data engineering is popular for offering data warehousing services to several business enterprises. However, the same type of database cannot be suitable for all kinds of data workloads. Experts predict that organizations will shift from data warehouses to data lakes where different databases and tools are individually organized and grouped into a unified setup. This can make the data architecture cost-effective and increase its performance. 4. Data Teams with Higher Specializations Though data engineers are in short supply due to the complexity of the job, data teams will continue to expand and include professionals with more specializations. For example, the data teams will have data engineers, data analysts, data scientists, analytical engineers, etc., to handle different aspects of establishing and using the data architecture in an enterprise. DevOps managers, finance managers, data reliability engineers, data architects, data product managers, etc., are other specializations we will see in future data teams. 5. Metrics Layers in Data Architecture In traditional data pipelines, the metrics layer (also called the semantics layer) is in the middle, between the ETL (extract, transform, load) layer and the cloud data warehouse. It defines the metrics for the values in the data tables and ensures consistency to eliminate errors during business analytics. Experts predict that the metrics layer will have an addition of a machine learning stack that has its own infrastructure. The ETL layer will continue to do its job, but the data will flow through the machine learning stack, which will help data scientists choose the right metrics for the given data. One day, the metrics layer and the ML stack will be combined to work as a single automated unit. 6. Data Mesh The concept of data mesh is one of the emerging DE trends discussed by many top companies. This new architectural model is said to help organizations overcome the limitations of traditional data warehouses and centralized data lakes. Date mesh is the decentralization of data governance and ownership. As discussed in the previous trends, domain-specific data platforms, tools, and databases will be established for greater efficiency. The idea is to build resilient, dynamic, and agile data pipelines that offer more autonomy, interoperability, and control to every member of the data team. However, establishing a data mesh requires more skills and tools. However, centralized data warehouses will continue to exist until enterprises can successfully build and deploy data mesh architecture. 7. Increase in Success of Machine Learning Models In 2020, a report by Gartner shows that ML models had only a 53% success rate. That too when they were built by companies with strong AI foundations and prior experience. It means even three years ago, only half the machine learning models could be deployed accurately and effectively. However, the success rate has been increasing over time. Soon, a greater percentage of the ML models can be successfully deployed by organizations. Of course, this will be possible when businesses overcome challenges such as misalignment of needs and objectives, overgeneralization, testing, validating issues, etc. 8. Changes in Cloud-Premises Architecture The architecture for data flow within an enterprise usually combines three different software applications. Databases from different departments (CRM, CDP, etc.) are connected to the data warehouse. The business intelligence and data visualization tools are connected to the other end of the data warehouse. Data flow occurs only in one direction. However, in modern data engineering, the data flow will occur both ways. The next-gen cloud data architecture will be bi-directional and allow data

Yashica Chopra
April 26, 2023
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Data Engineering Services: A Modern Business Essential

Data engineering focuses on the practical applications required to establish a flexible, scalable, and agile data infrastructure in an enterprise. It is the most vital part of adopting the data-driven model to make business decisions. Here, we’ll discuss data engineering services and their role in modern businesses. Data is an integral part of the IT industry. Over the years, users have generated large volumes of data, which is being collected by businesses to fine-tune their products/ services and enhance customer experience. Statistics show that an average user generates 1.7 MB of data per second. Around 97.2% of businesses are investing in artificial intelligence and big data, though a study shows that many companies analyze only up to 40% of the data they collect from all sources. Another interesting observation is that companies that actively use big data analytics increase their profits by around 8%. Wouldn’t it be beneficial for businesses to further utilize data to amplify their profits? So, how can it be done? The answer is big data engineering services. In simple terms, data engineering is the process of streamlining data collection, storage, and analytics to get more insights from datasets. However, it is an elaborate process that requires IT infrastructure and expert skills. Data engineering is the foundation of building the data analytical model in an enterprise. Businesses partner with data engineering companies to adopt the data-driven model for effective and faster decision-making. Enterprises find it a cost-effective solution to rely on offshore data engineering service providers to derive actionable insights using AI and big data analytics. In this blog, we understand the process in detail and explain why data engineering is needed for every modern business around the world. What is Data Engineering? Data engineering is the process of collecting and validating data to ensure high-quality datasets are available for data scientists. Data engineering is a vast domain that includes a range of skills, tools, and applications. It is a combination of several modules like data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management. A data engineer should maintain the data infrastructure that supports business intelligence solutions. They should work with programming languages, database software, machine learning, and artificial intelligence algorithms. They can work in small teams that focus only on ingesting data into systems or be a part of large teams offering data engineering services & solutions and collaborate with data scientists and database administrators to streamline the data pipeline in mid and large-sized enterprises. What are Data Engineering Services? Data engineering services are varied and versatile. The top data engineering services companies offer end-to-end solutions to design, build, deploy, and maintain a seamless system that collects, cleans, stores, processes, analyzes, and visualizes data through BI tools. The following are some important services offered by the companies: Data Ingestion Data ingestion is the process of moving or replicating data from sources to the cloud storage platform. It is a prominent step in the modern data stack. It determines the quality and type of data a business uses for analytics. Data engineers have to determine if this process will take place in batch mode or in real-time. Factors like cost and resource allocation play a vital role in finalizing the time frame for data ingestion. Data Storage Data storage management is another crucial part of data engineering services. The data collected from multiple internal and external sources has to be stored in a central database for further processing and analysis. Data engineers have to design the best data storage method that allows employees to access datasets in real-time. Data storage solutions can be on-premises or on the cloud. Businesses can even use a combination of both. Data warehousing and data lakes are two popular methods used to store vast amounts of data. Businesses offer Azure data engineering and AWS data engineering services to build and customize cloud data storage centers. Data Integration Data integration is a key data engineering service as it sets up the necessary connections between different systems, apps, and databases. It is the process of setting up the connection between the central database with the input and output channels. For example, the sources have to be connected with the data warehouse to collect data. Similarly, the data warehouse has to be connected with ERP systems and BI tools to run analytics and share data visualizations with the end user. Data Processing Data processing is the process where large datasets are cleaned and manipulated to derive useful information. Data from the data warehouse or data lakes are retrieved, classified, cleaned, and formatted to make it ready for analysis. This stage helps remove errors and duplicate data to increase the accuracy of the derived insights. It is yet another important part of data engineering services as low-quality data can result in incorrect insights which can lead to wrong business decisions. Business Intelligence Business intelligence is a vital part of the process. This is where data is converted into meaningful information and presented in graphical reports. Data engineers have the responsibility to identify the right BI tool based on business requirements and customize it accordingly. The dashboards also have to be set up and integrated with the rest of the infrastructure to provide data visualizations in real-time to employees across all departments. How Does Data Engineering Help a Business? Data engineering or information engineering is the foundation for adopting and using the data-driven model in an enterprise. Data engineering and analytics go hand in hand and have to be aligned at all times to ensure that the top management and employees can access actionable insights at any given point in time. This allows them to make faster decisions based on reliable reports rather than guesswork. Once data engineers set up the data architecture (systems and connections), data scientists can perform the analytics and share reports. Artificial intelligence tools and machine learning algorithms are used in the process to ensure the seamless and real-time flow of data from one system to another. Typically, data and engineering services help businesses in the following ways: Data engineering companies also offer data analytical