A data lake is a centralized data repository that can store massive amounts of structured, semi-structured, and unstructured data from diverse sources. Unlike a data warehouse, a data lake can store data in its raw format for analytics and machine learning use cases.
Data lakes build a centralized view of enterprise data while still preserving granularity, unlike traditional enterprise data warehouses which transform data into schemas optimized for business reporting.
A data lake ingests bulk data from sources like databases, IoT devices, social media feeds. The data is stored in native formats like JSON, Parquet, Avro along with metadata. This allows running analytics on both raw and transformed data using data processing engines and time-series databases.
Data lakes utilize scalable storage like HDFS along with fast data processing engines like Spark for big data analytics. They help scale analytics by removing overhead of schema-on-write models.
Data lakes provide a way to cost effectively store massive amounts of enterprise data in various structures and formats. This data can then fuel analytics, machine learning and AI to drive predictive insights, sentiment analysis, recommender systems etc.
Use cases include web analytics based on server logs, IoT analytics combining sensor data, analytics combining transactional data with social data. Data lakes are crucial for data science initiatives across industries.
A data lake is a centralized repository that can store large amounts of structured and unstructured data. Its key components provide capabilities for scalable storage, data ingestion, metadata management, security, and analytics.
Data lakes can store raw, unprocessed data on a large scale and are well-suited for certain analytics use cases:
However, building and managing data lakes comes with inherent complexities:
A data warehouse is a centralized data management system designed to enable business reporting, analytics, and data insights.Read more ->
A data orchestrator is a middleware tool that facilitates the automation of data flows between diverse systems such as data storage systems (e.g. databases), data processing engines (e.g. analytics engines) and APIs (e.g. SaaS platforms for data enrichment).Read more ->
A data processing engine is a distributed software system designed for high-performance data transformation, analytics, and machine learning workloads on large volumes of data.Read more ->
Our CEO Ozan recently joined an episode of the Streaming Caffeine podcast — Streaming Caffeine E10: Ozan from Synnada, about Arrow Datafusion, Rust, Databases, SQL, AI — to discuss our perspective on DataFusion and the future of data infrastructure.