A streamlined and automated data pipeline is the core of a well-built IT infrastructure and results in proactive decision-making. Here, we’ll discuss the detailed guide into a modern approach to data management pipeline and how to build a robust data system in your enterprise.
Data is the core of every business in today’s world. You can no longer ignore the importance of data and its role in running an establishment. Whether a startup or a large enterprise with a presence in multiple countries, data holds the key to insights that help make better decisions. It doesn’t matter which industry you belong to. Business and third-party data are necessary to make informed choices in all verticals.
As per Statista, the total amount of data created and consumed globally was 149 zettabytes in 2024 and is expected to be over 394 zettabytes by 2028. But how will you manage large amounts of data in your enterprise? How will you store it when more data is added every day? How will you clean and organize the datasets? How will you convert raw data into actionable insights?
That’s where data management and data engineering help. Data management is the process of collecting, ingesting, preparing, organizing, storing, maintaining, and securing vast datasets throughout the organization. It is a continuous and multi-stage process that requires domain expertise and knowledge. Luckily, you can hire a data engineering company to provide end-to-end services for data management.
In this blog, we’ll learn more about data management’s process, tools, and pipeline and how it can benefit your business in the long run.
According to a report by IOT Analytics, the global data management and analytics market is predicted to grow at a CAGR (compound annual growth rate) of 16% to reach $513.3 billion by 2030.
The modern data management workflow relies on various tools and applications. For example, you need a repository to store the data, APIs to connect data sources to the database, analytical tools to process the data, etc. Instead of leaving the data in individual departmental silos, the experts will collect the data and store it in a central repository. This can be a data warehouse or a data lake. Typically, these can be on-premises in physical units or cloud servers in remote locations (data centers). The necessary connections are set up for data to be sent from one source to another. These are called data pipelines.
The data management process broadly includes seven stages, which are listed below.
Data architecture is the IT framework designed to plan the entire data flow and management strategy in your business. The data engineer will create a blueprint and list the necessary tools, technologies, etc., to initiate the process. It provides the standards for how data is managed throughout the lifecycle to provide high-quality and reliable outcomes.
Data modeling is the visual representation of how large datasets will be managed in your enterprise. It defines the relationships and connections between different applications and charts the flowchart of data movement from one department to another or within the departments.
Data pipelines are workflows that are automated using advanced tools to ensure data seamlessly moves from one location to another. The pipelines include the ETL (extract, transform, load) and ELT (extract, load, transform) processes. These can be on-premises or on cloud servers. For example, you can completely build and automate the data management system on Microsoft Azure or AWS cloud.
Data cataloging is the process of creating a highly detailed and comprehensive inventory of the various data assets owned by an enterprise. This includes metadata like definitions, access controls, usage, tags, lineage, etc. Data catalogs are used to optimize data use in a business and define how the datasets can be utilized for various types of analytics.
Data governance is a set of frameworks and guidelines established to ensure the data used in your business is secure and adheres to global compliance regulations. This documentation has to be followed by everyone to prevent unlawful usage of data. The policies ensure proper procedures for data monitoring, data stewardship, etc.
Data integration is where different software applications and systems are connected to collect data from several sources. Businesses need accurate and complete data to derive meaningful analytical reports and insights. This is possible by integrating different third-party systems into the central repository. Data integration also helps in building better collaborations between teams, departments, and businesses.
Data security is a vital part of the data management pipeline and a crucial element in data engineering services. It prevents unauthorized users and outsiders from accessing confidential data in your systems. It reduces the risk of cyberattacks through well-defined policies. Data engineers recommend installing multiple security layers to prevent breaches. Data masking, encryption, redaction, etc., are some procedures to ensure data security.
The data management pipeline is a series of steps and processes required to prepare data for analysis and share data visualizations with end users (employees) through the dashboards. It automates the data flow, increases system flexibility and scalability, improves data quality, and helps in delivering real-time insights.
The first step in building a data management pipeline is to know what you want to achieve. Focus on the short-term and long-term goals to build a solution that can be scaled as necessary. Discuss the details with department heads and mid-level employees to consider their input. Make a list of challenges you want to resolve by streamlining the data systems. Once done, consult a service provider to understand the requirements and timeline of the project. Aspects like metrics, budget, service provider’s expertise, etc., should be considered.
The next step is to identify the sources to collect the required data. These will be internal and external. Determine what type of data you want (unstructured, semi-structured, or structured), how frequently new data should be uploaded to the repository, how much to pay for data, what compliance regulations to follow, and so on. The data engineering company will handle this on your behalf.
When the basics are in place, you can start working on the design for the data pipeline architecture. Make this detailed and clear to reduce the risk of errors and confusion. The blueprint should include an overview of the entire structure of the data pipeline, the technologies required to build this, and the various data security regulations you should follow. Taking time at this stage will ensure the design is foolproof and reliable. Build for the future by making space for flexibility and scalability.
How do you intend to feed data into your data pipeline? How frequently can you collect data from the sources? Typically, businesses opt for batch processing or streaming processing. As the names suggest, batches are scheduled for a fixed frequency (once an hour to once a week/ month). Streaming works in real-time and is useful for advanced analytics. Both methods have their benefits. The right choice depends on your business needs and budget.
The collected data has to be transformed and made ready to be sent to analytical tools to derive insights. This is also a part of the data engineering services. Generally, data transformation deals with converting raw data in different formats to a structured and consistent format. This removes duplicates and corrects the errors. Data from multiple sources are combined to enhance the results as it allows for a much wider perspective.
Where will you store the vast amounts of data collected from the sources? Previously, businesses relied on silos in departments. However, this led to data becoming outdated with a lot of duplication. However, by building a central repository, you can store all the data in a single unit and provide access to employees to use the data as necessary. This repository can be a data warehouse or a data lake. It can be built on-premises or on the cloud. Considering the large quantities of data collected for analytics, it is recommended to store it on a secure cloud platform.
Building and deploying the data management pipelines is not the end of the process. In fact, it’s the beginning. The setup has to be continuously monitored to make sure the data flow is seamless and accurate. Different applications in the network have to be optimized to reduce the usage of resources and minimize costs. Regular maintenance checks and upgrades are essential to prevent glitches and bugs in the data management system. Fortunately, the data engineering company will take care of the support services for as long as you want (depending on the terms of the agreement).
As mentioned earlier, data governance is one of the key components of the data management process. The service provider will create the documentation and share it with your employees to help them use the systems effectively. The company may also provide training services to empower your decision-makers in using the data system and insights for day-to-day work. Additionally, the documentation provides guidelines for data security and data privacy, as well as for troubleshooting minor errors. New members can be onboarded to the team quickly by sharing the documentation with them.
The last stage of the data management pipeline deals with how the data is provided to the end user. Here, end users are employees (internal and augmented) and stakeholders who have business decisions. The data warehouse (or data lake) is connected to data analytics tools which are connected to personalized data visualization dashboards accessed by the employees. When an employee sends a query, the analytics tool uses the data in the repository to find the answer to share the insight through the dashboard. The process occurs internally, so employees only have to input a query to get the output.
A data pipeline has some key components to streamline the data management process and workflow. These include the following:
The sources for data are the initial or starting points of the data pipeline. From where do you collect the raw data? These sources are internal (departments, offices in different locations, etc.) and external (social media, third-party websites, data vendors, partners, etc.).
Data ingestion brings the data from sources into the data pipeline. This can be done through batch processing, data streaming, etc., depending on how frequently you want to collect and update the data in your repository.
The processing stage involves more than one step. It is a series of actions like data cleaning (to remove duplication and errors), data standardization (formatting and structuring), data aggregation (combing data from different sets and assigning tags), and making data ready for analytics.
The storage center holds raw and processed data. It is connected to data sources as well as analytical and other tools that share insights with employees through the data visualization dashboards.
Data analytics is the process of extracting insights, identifying patterns and trends, and discovering correlations from data to make informed and data-driven decisions. Various tools like Power BI, Tableau, Apache Spark, etc., are used for data analytics. Advanced analytics require artificial intelligence tools and machine learning models.
Data visualization presents the insights in easy-to-understand graphical reports and delivers them to end users (employees) through personalized dashboards. Data engineering companies will set up the dashboards based on your KPIs (key performance indicators).
Tracking the health of the data pipeline is also a key part of the process. This is to ensure that the data infrastructure is reliable and functions without errors. The orchestration tools manage workflows, connections, automation, troubleshooting, etc.
The data pipeline architecture uses either of the two popular models:
ETL stands for extract, transform, and load. It is the order in which the data from sources is sent to the data warehouse. In this method, data is extracted from the sources, transformed (cleaned and formatted), and loaded into the repository.
ELT stands for extract, load, and transform. The order slightly changes from the previous model. After data extraction, it is directly loaded into the data lake and then transformed as required before being sent to the data analytics tools.
Data management requires various tools to handle functions like ingestion, cleaning, transforming, archiving backup and recovery, encryption, analytics, reporting, etc. An easier way to access the tools is by hiring Azure data engineering or AWS data engineering services from reputed service providers. Azure, AWS, and Google dominate the market but there are various other vendors offering a plethora of similar tools and technologies.
For example, Microsoft Azure has Data Studio, Data Factory, Azure SQL Database, etc., to facilitate seamless data management in different environments. A certified Microsoft partner company will have full access to these tools and use them to build, deploy, and maintain the data management pipeline for your business. A few third-party tools are Panoply, Informatica PowerCenter, Fivetran, Alooma, etc.
Building and maintaining the data management pipeline is highly beneficial for every business as it can help with the following:
Managing vast amounts of data is a common struggle for many organizations. Partnering with a reputed data engineering and management company is an effective way to overcome challenges and find a long-term solution.
It’s vital to adapt to the changing market conditions by investing in the latest tools and technologies to manage business data. This can not only streamline internal operations but also give you a competitive edge and increase revenue.
Leveraging data management pipelines through data engineering services will help businesses unlock the full potential of their data and derive meaningful insights in real time. It makes the organization more resilient, scalable, and ready for the future.
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Fact checked by –
Akansha Rani ~ Content Creator & Copy Writer
