As a data engineer, it’s your responsibility to handle and process data effectively. The type of data you encounter in your work can vary, but you will no doubt encounter databases, data lakes, and data warehouses at some stage along your journey as a Data Engineer. In this blog post, I briefly highlight the differences between databases, data lakes and data warehouses.
Database
A database is a structured collection of data that is organised and stored in a specific way, making it easy to retrieve and manipulate. Databases are typically used to store transactional data, such as customer orders, financial records or product information. There are different types of databases, including relational databases (such as MySQL, SQL Server or Oracle) and non-relational databases (such as MongoDB or Cassandra).
Data Lake
A data lake is a large, centralised repository that allows you to store all your structured and unstructured data at any scale. Unlike a database, a data lake doesn’t impose any structure on the data you store in it. This makes it an ideal solution for storing large amounts of raw data that may not have a clear structure or purpose yet. The data lake is designed to handle a vast amount of data by providing a single source for storage, processing, and analysis.
Data lakes are typically used for storing large volumes of raw data, such as log files and sensor data, and for performing batch and real-time processing on that data. Many companies use cloud storage services such as Google Cloud Storage and Amazon S3 or a distributed file system such as Apache Hadoop distributed file system (HDFS).
Data Warehouse
A data warehouse is a large, centralised repository of data that is used for reporting and analysis (It’s normally some sort of DBMS). Data warehouses are designed to support the efficient querying and analysis of data, and are optimised for fast query performance. Data warehouses typically store structured data that has been cleaned, transformed, and integrated from a variety of sources (your classic case of ETL). They are optimised for ad-hoc querying, reporting and analysis, and are often used to support business intelligence and decision-making.
In summary, a database is a collection of structured data used for storing and accessing data efficiently, a data lake is a central repository for storing and processing large volumes of raw data, and a data warehouse is a large repository of structured data used for reporting and analysis.
