Data lakes can save your enterprise money, but they come with their own set of challenges. These tips will help you overcome three of the biggest issues.
Originally implemented as a storage and management solution, the data lake has morphed into a powerful technology that provides flexibility, scalability, and cost-effectiveness for data storage and analysis. One of the key challenges of using a data lake is processing data so it is easily usable by downstream data consumers. Automation can play a crucial role here, making it easier to reliably manage data movement to a data lake.
Data lakes provide cost-effective storage for enterprises, with support for large volumes of data at a lower price than a traditional data warehouse. Data lakes make it easier to store and analyze data from multiple applications with tremendous flexibility. However, these qualities can also create compliance and usability issues. The complexity of managing data pipelines and security is a top concern for many companies and can hold organizations back from reaping all the benefits of a data lake. Landing data in an unstructured form makes it easier to store, but companies still need to organize the data with a solid infrastructure to efficiently use it in downstream applications.
Although data lakes provide unparalleled flexibility, traditional data warehouses offer benefits such as an increased focus on ACID compliance, including consistency and reliability. Many companies are initially landing their data in a data lake, but eventually find themselves moving the data to a warehouse to address challenges related to pipeline building, analytics, and governance.
To help overcome these challenges and automate data movement to a data lake, companies can focus on the following areas:
Pipelines
Building and maintaining pipelines is often the most challenging and finicky part of the process. It involves ensuring that the data captured is funneled properly, especially when dealing with schema and API changes from external data sources. Regular pipeline maintenance is vital, including removing orphaned files, along with pulling and deleting snapshots. Prioritizing table maintenance within the data lake itself is crucial for pipeline efficiency. An organized pipeline workflow allows data engineering teams to focus on higher-value tasks, and lets analytics users easily manage their specific requirements without spending extra time just trying to discover the right data.
Data catalogs
A metadata catalog is invaluable for searching the depths of a data lake for specific information. A catalog allows organizations to define tables, set user-level access controls, and quickly locate multiple data sources (which, in turn, helps an enterprise address governance and privacy requirements such as GDPR data-deletion requests). Deploying a data catalog can seem like an additional expense, but that catalog can provide essential change tracking and indexing capabilities, saving significant time and computing power when retrieving data in the future.
When selecting a data catalog and file format, consider the end-user applications and the broader corporate infrastructure platform so your data catalog fits in with the rest of your cloud services. Choosing between options such as Apache Iceberg and Delta table formats depends on which downstream applications and tools your company uses.
Automation of data processing
Automating data processing tasks, including data normalization, deduplication, and cleaning up small, orphaned files, is critical for maintaining data quality and accuracy in the data lake environment. Start by creating an inventory of the types of data gathered, frequency of data updates, and the specific data consumers within the organization. This inventory helps optimize data updates based on the downstream services’ needs and ensures timely data availability.
Striking the right balance between low latency and cost efficiency is crucial, but knowing you have a reliable pipeline of data is essential to improve governance and security. Automating the management of data transfer into a data lake through pipeline management, data cataloging, and automation will improve data landing, standardization, and data quality, and will deliver actionable insights with fewer interim hoops to jump through.
A Final Word
By embracing automation, organizations can maximize the potential of their data lakes, enhance efficiency, and focus on leveraging data-driven insights to drive business success. Automation is essential for addressing the challenges associated with data lakes and enabling efficient data movement and management. By focusing on pipeline building and maintenance, deploying a data catalog, and automating data processing tasks, organizations can overcome these challenges.
