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big data management

By Cameron Hashemi-Pour

What is big data management?

Big data management is the organization, administration and governance of large volumes of both structured and unstructured data. The goal of big data management is to ensure a high level of data quality and accessibility for business intelligence and big data analytics applications.

Companies, government agencies and other organizations use big data management strategies to deal with fast-growing data pools, typically involving many terabytes or even petabytes stored in various file formats. Effective big data management helps locate valuable information in large sets of unstructured and semistructured data from various sources, including call records, system logs, internet of things and other sensors, images, and social media sites.

Most big data environments go beyond relational databases and traditional data warehouse platforms to incorporate technologies that are suited to data processing and storing nontransactional forms of data. The increasing focus on collecting and analyzing big data is shaping new data platforms and architectures that often combine data warehouses with big data systems.

As part of the big data management process, companies must decide what data must be kept for business or compliance reasons, what data can be disposed of, and what data should be analyzed to improve business processes or provide a competitive advantage. This process requires careful data classification so that, ultimately, smaller sets of data can be analyzed quickly and productively.

Top challenges in managing big data

Big data is usually complex. In addition to its volume and variety, it often includes streaming data and other types of data that are created and updated at a high velocity. As a result, processing and managing big data are complicated tasks. For data management teams, the biggest challenges faced with big data deployments include the following:

Benefits of big data management

When done correctly, big data management can yield long-term benefits, including the following:

Best practices for big data management

Big data management sets the stage for successful analytics initiatives that drive better business decision-making and strategic planning. What follows is a list of best practices to adopt in big data programs to put them on the right track:

Big data management tools and capabilities

There's a variety of platforms and tools for managing big data, with both open source and commercial versions available for many of them. The list of big data technologies and analytics tools that can be deployed, often in combination with one another, includes distributed processing frameworks Apache Hadoop and Apache Spark, stream processing engines, cloud object storage services, cluster management software, Structured Query Language (SQL) query engines, data lake and data warehouse platforms, and NoSQL databases.

To enable easier scalability and more flexibility, big data workloads are often run in the cloud, where businesses can set up their own systems or use managed services offerings. Big data management vendors include the leading cloud platform providers: AWS, Google and Microsoft.

Mainstream data management tools are key components for managing big data. They include data integration software supporting multiple integration techniques, such as the following:

Data quality tools that automate data profiling, cleansing and validation are commonly used in the field of big data science too.

The future of big data management

Among the various approaches and tools that will help organizations deal with big data challenges in the future are the following:

Big data management is crucial for organizations that deal with vast data volumes, but big data must be culled from various sources first. Discover how the big data collection process works, along with techniques and challenges organizations need to know to be successful at it.

21 Mar 2024

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