Data Lakes vs. Data Warehouses
A data lake is a centralized repository for the storage of structured and unstructured data. The advantage of a data lake is having data for different types of analysis, from large-scale processing to dashboards, to guide better decisions.
A data lake is not the same as a data warehouse. A data warehouse, in most cases, has a defined structure and a relational map or schema, making it better suited for handling transactional business cases. A data lake begs to be explored, refined, and analyzed. It’s built for real-time data acquisition in any amount, provides for multiple users, tools, and applications, and provides a steady platform for machine learning and analysis.
Don’t Turn Your Data Lake into a Data Swamp
Does your data lake contain real-time data, and is it refreshed on a continual basis? Are there bottlenecks or restrictions that force long wait times? Are there defined methods for cataloging and security? Is your data lake more of a “swamp” than a useful source to power your business? To avoid your data lake becoming a data swamp, your approach should include the following considerations.
- Avoid turning your data lake into a junk drawer. Don’t turn your data lake into a catch-all bin for every piece of data that comes from your CRM, transactions, or processes. Your data pipeline processes that flow into the data lake should contain identifying information: metadata, which should include data lineage, data structure, data age, and other important identifiers.
- Choose the right tools to connect analytics to the data lake. The right tool can help turn data lakes from unstructured pools into responsible channels of information. Tools should have support for SQL language or ODBC or JDBC connectors, which act as an intermediary between the data and the user.
- Check data lake performance on a routine and not sporadic basis. A data lake is a large information source and should be placed on a network or cloud storage which can handle constant pipelines, transactional exchanges, and massive analysis queries. It is recommended to connect your data lake to a systems monitoring platform, such as DataDog, which can check hardware and network performance around the clock and perform diagnostic reviews.
- Split your data lake into partitions. Not necessarily a structure so much as a subdivision, splitting the overall size of a data lake into partitions reduces network connectivity strain and load times and helps users find data slices for their respective work.
Getting Hydroelectric Power From Your Data Lake
Once you have mastered implementing your data lake, there are steps you should take to implement and maintain quality and reliability in your data lake.
- Build data lakehouses. A data lake is a generalized, unstructured store of your data from multiple data sources. A data warehouse is a structured repository of data used for transactional processing. Think of a data lakehouse as a data lake with a transactional structured data layer (like a data warehouse, but not holding any data.) The advantage of using a lakehouse is that analytics engines, such as Google’s BigQuery, can be deployed to support the transactional layer and improve the quality of the data stored in the lake.
- Use standardized data formats. From the outset, you should establish data formats for data that resides in the data lake. This is not the same as a data schema which represents a structure. Using standardized formats for dates, times, currency, addresses, etc., can go a long way to improving the quality of data before it is pipelined into the data lake.
- Begin with a data governance policy. Although a data lake is an unstructured island of data, emphasizing data governance policies before pipelining into the data lake establishes the quality of data before it is sent to analytics engines, data tools, spreadsheets, and so on. It is better to discuss and establish policies first that pertain to handling or analysis before running into a big headache later. In addition, governance policies should be continually reviewed and adjusted.
- Avoid data silos. Although it seems obvious, a data lake works best as a wide-open workspace rather than a series of silos lumping your data by source, type, field, etc. Data lakes are supposed to be unstructured. To get the best use from a data lake, use tools to manage the heavy lifting required to wrangle large data sets.
- Consider data partitioning. Just like trying to find a copy of your favorite book in the library, data partitioning builds folders that help data researchers or marketers locate data using logical groupings. Foresight is the answer here, as building a data lake should also come with questions about how the data lake may be used. If the most recently updated data is crucial, then organizing partitions (folders) by the most recent date (and then working backward) eliminates hunting and pecking. If the type of data is important (for example, by gender or by customer type), then grouping the data via these categories works for the researcher instead of against it.
- Hire a data engineer! Set your data lake up for success by hiring a good data engineer to help build your data lake. A good data engineer will understand your business needs and the purpose of a data lake and propose best practices and solutions. You wouldn’t build a hydroelectric dam without consulting an engineer; consider hiring a data engineer to build your next data lake. Better still, consider Terazo!
Terazo has a fundamental understanding of how data quality leads to a clearer picture of your business strategy. To learn more about how Terazo provides the best in data engineering, contact us at email@example.com.
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