What is data lake? A beginner's guide for everyone

A data lake is a platform that stores structured, semi-structured, and unstructured data from various sources such as mobile apps, IoT sensors, videos, audio, etc. It differs from a database in that it can store historical and current data without requiring a predefined schema. Data lakes are flexible for analytical use cases like data science and machine learning (ML), big data processing, and real-time analytics. They contain no predefined schema but work on a schema-on-read basis. Data scientists and analysts use data lakes for ad-hoc analysis and operational reporting. Data sources can include mobile apps, IoT sensor data, the internet, internal business applications, etc., in different formats such as unstructured data like images, video, emails, audio, semi-structured data like CSV or JSON files, and structured data in table formats. Data lakes are architected with a resource management facility, access management service, metadata management layer, ELT pipelines, and analytical tools. They consist of five critical components: data ingestion, data storage, data transformation, data serving, and data exploration. Data lakes store data using object storage, which is different from file and block storage methods. Data scientists, data analysts, financial organizations, tech giants, retailers, etc., use data lakes to improve operational efficiency. They can be used for multi-channel marketing, healthcare, finance, and other industries. The importance of data lakes for businesses lies in providing a 360-degree view of customers, improving business operations, and enabling ML and AI capabilities. There are four types of data lakes: enterprise data lake (EDL), cloud data lake, Hadoop data lake, and real-time data lake. Several data lake platforms exist, including AWS, Hadoop, Microsoft Azure, Oracle, and Google Cloud. Data lakes have both advantages like flexibility, scalability, enabling ML and data science, and disadvantages like complex access management, costly maintenance, and becoming a data swamp without proper governance.