What is a Data Lake?
What’s the world’s biggest lake? If you said the Caspian Sea, you’re correct. But you’re also correct if you said a data lake. This technology stores trillions of files, making it one of the best ways to store big data at scale. But what is a data lake, exactly? And when will you use one in your data science or engineering career? Learn more below.
Data Lakes, Explained
A data lake is a central repository that stores structured and unstructured data in bulk. Moving data from various other systems—think databases like Oracle and customer relationship management (CRM) systems like Salesforce—to a data lake allows users to analyze that data and produce business insights.
You’ll almost certainly use a data lake at some point in your data science or engineering career. For example, this technology lets scientists understand what data exists in the organization they work for so they can catalog it, index it and analyze it. All the data scientists need for analysis will be in one single system, making it easier for them to do their jobs.
Data vendor Hitachi (formerly Pentaho) coined the term data lake in 2011. Since then, businesses in multiple industries have invested in this innovation to centralize, store and analyze data. Examples of data lake technologies include Cloudera, AWS Data Lake and Google Cloud.
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How Do Data Lakes Work?
Data lakes rely on a data integration process called Extract, Load, and Transform, or ELT. It involves creating data pipelines to extract data from its source—say Salesforce—load it into a data lake and then transform the data into the appropriate format for data analysis. Either data engineers complete this process manually, or ELT tools do much of the hard work for them.
ETL (Extract, Transform, Load) is another data integration process better suited for data warehouses, which, like data lakes, store vast amounts of big data at scale. The difference between these methods is that ETL requires users to transform data before loading it into a warehouse.
Benefits of Data Lakes
Here are some of the advantages of data lakes:
Silos exist when computer systems in different business departments can’t communicate with each other. Perhaps because these systems are too old. Or there’s no way of connecting them. Moving data from these disconnected or “disparate” systems to a data lake allows business users to access that data in a centralized repository, which can improve data management and visibility.
A Single Source of Truth
Moving information from different sources to a data lake creates a single source of truth for all the data in an organization, making it easier to manage and analyze that data. A single source of truth translates to increased productivity and transparency, allowing businesses to generate a 360-degree view of their data sets.
By moving data to a data lake, users can run that data through business intelligence (BI) tools and generate insights into performance and productivity. Data scientists can identify patterns and trends in data sets and help businesses make smarter decisions.
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Negatives of a Data Lakes
Here are some of the downsides of using data lakes:
Data lakes aren’t the easiest things to use and require data engineers to manage them. Otherwise, it’s difficult to run data through BI tools and generate insights. One problem that impedes successful analysis is inconsistent data structures that transfer over to a data lake.
As previously mentioned, data lakes rely on the ELT process, which involves loading data before transforming it into the correct format for analysis. That can be difficult when users need to comply with data governance legislation like GDPR and CCPA. Data enters a lake in a format that might put customers’ personally identifiable information at risk.
A data lake centralizes, stores and analyzes data in bulk, making it a useful tool for various businesses. As a data scientist or engineer, you’ll use this technology to help companies move data from sources and generate valuable insights.
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