Tomer Shiran, co-founder of Dremio, talks about managing data inside a data lake, historical changes and motivations for managing data as a data lake, and the common tools and methods for ingestion, storage, and analytics on top of the underlying data.
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Quick takeaways
Data lakes provide a scalable and cost-effective solution for storing and ingesting large volumes of data into a centralized repository.
The evolution of data storage formats in data lakes, such as the transition to column-oriented formats, has improved performance and query response times.
To maintain an organized and up-to-date data repository, minimizing data copying and utilizing virtual datasets and data reflections can help prevent data swamps.
Deep dives
The Evolution of Data Warehouses
Data warehouses emerged as a centralized solution for analyzing data from multiple sources. Originally, data warehouses were created using the same technology as operational databases, but they provided a separate space for analysis to avoid impacting the performance of operational systems.
The Rise of NoSQL and Semi-Structured Data
The rise of NoSQL databases like MongoDB offered developers a more productive way to work with nested data structures compared to traditional relational databases. Additionally, the need to analyze data beyond traditional, structured formats led to the emergence of semi-structured data sets stored in data warehouses.
The Birth of Data Lakes
Data lakes were initially created to store and ingest large volumes of data more easily into a centralized repository. The affordability and scalability of cloud storage services like AWS S3 made data lakes an attractive option. Early data lakes focused on batch processing, but the flexibility led to the creation of various computational engines and the separation of compute and storage in the data lake architecture.
Data Storage Formats: From JSON to Column-Oriented
The evolution of data storage formats in data lakes moved from JSON and XML to more optimized column-oriented formats like Parquet and Avro. Column-oriented formats improve performance by allowing efficient compression and enabling query optimization based on column-based data access. Column storage also allows data lakes to handle large volumes of structured data efficiently and provides faster query response times.
Data Lake Challenges and Management
Challenges of data lakes include the risk of turning into data swamps with unmanaged and outdated copies of data. To prevent this, minimizing data copying and utilizing virtual datasets and data reflections can help maintain an organized and up-to-date data repository. Operational aspects of data lakes involve the management of data teams responsible for handling data ingestion, transformation, permissions, and security. Cloud infrastructure and advanced tools like Dremio have simplified operational tasks by providing elasticity, caching mechanisms, and unified metadata catalogs.
Tomer Shiran, co-founder of Dremio, talks about managing data inside a data lake, historical changes and motivations for managing data as a data lake, and the common tools and methods for ingestion, storage, and analytics on top of the underlying data.
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