A Modern Approach to Scalable Data Management Pipeline
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. How the Data Management Process Works? 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. A Guide to Scalable Data Management Pipeline 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. Steps to Building a Data Management Pipeline Define Objectives and Requirements 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. Identify and List the Data Sources 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
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