Before your company is ready to use a data lake to easily store and access your business data, have a plan in place for managing it. Managing data lakes requires some strategy and consistency to ensure the data is well organized and ready for archiving or continual use.

In this article, we’ll go over what goes into managing data lakes and what you should consider before jumping in.

What is Data Lake Management?

Data lakes, like Azure or AWS, allow data of various types and sources to be stored together. Whether the data stored is structured, semi-structured, or unstructured, it can be housed in a data lake. This makes data lakes extremely convenient and useful. However, for this stored data to be clean, organized, and usable, you need the proper data lake management in place.

Data lake management enables you to collect, store, and quickly access data in a usable form whenever needed. It focuses on using the correct processes, tools, and governance strategies to store, optimize, and secure data. Some of the responsibilities include data ingestion, transformation, cataloging, and monitoring.

Why is Data Lake Management Important?

Data lake management is vital when storing data because every piece of data should be ready for use when you need it. Managing data lakes ensures your data is well-organized, easily identifiable, and searchable. It also makes data accessible and usable for AI, business intelligence, and machine learning tools.

Managing data lakes does require a little time, attention, and care. Consistent processes should be followed, and specific tools should be used to ensure that all data meet the same standards. This will help prevent “data swamps,” or collections of poor-quality, unusable data.

The Data Sources within Data Lakes

The two components of data lakes are storage and compute. You can store these on-premises, in the cloud, or in a combination of both.

You can store data from various sources in a data lake. These sources often include:

  • SaaS (software as a service) applications
  • Business applications
  • Enterprise data warehouses (EDW)
  • Flat files storing transactional data
  • Device logs
  • IoT sensors

Common Terms for Data Lake Management

Managing data lakes requires some familiarity with the concepts and terms below. These definitions can help you better understand the processes involved in data lake management.

  • Data ingestion: This describes when data is being taken in, either in one-time, batch, or real-time data loads using connectors.
  • Data governance/ data management: Governing and managing all your stored data.
  • Data lineage: This involves tracking data movement.
  • Data quality: This involves determining which data is most valuable based on accuracy and completeness.
  • Data security: This ensures data is kept secure using the right protocols.
  • Data auditing: This allows you to see what changes have been made to data, when changes were made, and by whom.
  • Data exploration: The identification of the data you’re trying to find.
  • Data discovery: You can organize and tag data so it can easily be discovered and interpreted.

Managing Data Lakes Effectively

The main elements of managing data lakes include data collection, migration, processing, integration, storage, organization, cataloging, security, governance, utilization, and management.

  • Data collection: This involves collecting data from various sources. Internal sources could include CRM systems, transactional databases, and others. External sources often include social media, IoT devices, etc.
  • Data migration: Migrating data from one system or platform to a new location, such as a data lake or another system, requires close attention so errors and formatting issues don’t occur and get transferred to the new location.
  • Data processing: Raw data often needs to be cleaned, transformed, and turned into a more usable format so it can be properly integrated. 
  • Data integration: Through integration, data from multiple sources can be viewed on a single platform. This enhances efficiency greatly and benefits departments across the business by providing data access to whoever needs it in their daily work.
  • Data storage: Businesses need to store their data securely. Factors like data volume, access frequency, price, and security requirements should all be considered before choosing a storage solution.
  • Data organization: When you collect data from internal and external sources, effective organization is key for finding, accessing, and analyzing data quickly and easily.
  • Data cataloging: Data cataloging is an inventory of data assets, which contains metadata needed for data to be findable.
  • Data security: This involves utilizing encryption, access controls, firewalls, security audits, backup systems, and more to keep data safe against different types of security breaches.
  • Data governance: Rules, policies, and procedures should be implemented for managing data. This ensures there is compliance over who can access, modify, and use data.
  • Data utilization: By using the right tools and following the right procedures, you can ensure your data is usable whenever necessary and ready for analysis.
  • Data life cycle management: Everything, including data, has a life cycle. From when it was first created to when it no longer serves a purpose and is deleted or archived, policies should be in place for how to manage data throughout its usefulness and beyond.
  • Master data management (MDM): Master data management ensures your primary business data, like customer and product information, is accurate, consistent, and accessible.

Data Management Challenges

While data management has many advantages, it also can present some challenges along the way.

A few of the most common challenges include:

  • Maintaining data management: Data continually gets added to your data lake. Without the right tools, it can be challenging to keep up with the data that’s constantly being added while maintaining and monitoring data quality and integrity.
  • Monitoring stored data: While data storage is usually cheap, the more data you’re storing, the more it will cost. Housing unnecessary data will only cost you more, so take into account what you’re storing and why.
  • Missed insights: If you don’t have a regular process in place, taking in large volumes of data can result in missed opportunities for insights and analytics.
  • Integration issues: Whenever you’re combining data from multiple sources, some issues can arise when the right storage and integration tools aren’t in place.
  • Employee training: As with any task and position, training is required for your employees to know how to manage data efficiently and correctly. This training takes extra time and expertise.
  • Data security: Data lakes house important and sensitive information, so effective security measures are a must. Make sure encryption and secure access are in place to keep data safe.
  • Continually evolving data requirements: Data requirements continually change, making it hard for businesses to keep up with compliance requirements.

Reach out for More Information

To simplify your data lake management, you don’t need to hire a data engineer or specialist. Whether you need to access your data from a data lake from multiple apps, migrate historical data to your new system, connect and work amongst multiple cloud-based apps, or perform seamless integrations from your on-prem systems to a cloud-based app, Popdock can provide the support and performance you need to work efficiently and surpass your goals.

Easily access external data in any of your apps. Learn how to increase accessibility and efficiency by using Popdock as your data access interface and virtual integration tool.

To go more in-depth on data lake management with Popdock, contact our sales team at sales@eonesolutions.com for more personalized help in answering your questions. They are always eager to provide help and answers for your data access and migration needs.