Data Lake

TL;DR

• Centralized storage that retains data in raw form, accommodating diverse types (text, images, audio, video, sensor data).
• Enables multiple teams to access and analyze data from many sources without pre-processing.
• Scales to large volumes and supports integration with analytics tools for batch or real-time processing.

Definition

A data lake is a large repository of structured and unstructured data that is stored in its raw format. This allows for the storage of a wide range of data types, such as text, images, audio, video, and sensor data, without the need for pre-processing or formatting.

Explanation

A data lake ingests data from multiple sources and keeps it in its original form, so different teams can apply their own processing and analysis workflows. Common storage platforms include distributed file systems and cloud object stores. Because the data is stored raw, a data lake accommodates diverse data types and analytic approaches, and it can grow as data volumes increase without major infrastructure changes. Data lakes also support access controls that can be applied at a granular level to protect sensitive data. They are often used alongside analytics engines (for example, Apache Spark or Apache Flink) to enable analysis and, where supported, real-time decision making.

Examples

Hadoop distributed file system (HDFS)

One example of a data lake is the use of a Hadoop distributed file system (HDFS) to store large amounts of data from multiple sources. In this scenario, a company may collect data from various sources such as web logs, social media, sensor readings, and transactional systems. The data is then ingested into the HDFS and stored in its raw format. This allows for easy access and analysis of the data by various teams within the organization, such as data scientists and business analysts.

Amazon S3

Another example of a data lake is the use of Amazon S3 as a data storage platform. In this scenario, a company may use Amazon S3 to store large amounts of data from multiple sources such as IoT devices, social media, and web logs. The data is then ingested into Amazon S3 and stored in its raw format. This allows for easy access and analysis of the data by various teams within the organization, such as data scientists and business analysts.

Use cases

• Analyzing text data from social media to understand customer sentiment.
• Analyzing sensor data from IoT devices to identify trends and patterns.
• Enabling multiple teams (for example, data scientists and business analysts) to access and analyze raw data without upfront formatting.
• Integrating with analytics tools to perform real-time analysis and decision making.

Related terms

• HDFS (Hadoop distributed file system)
• Amazon S3
• Apache Spark
• Apache Flink
• IoT
• Data scientist
• Business analyst