Building an Effective OSS Management Layer for Your Data Lake
Editor’s note: Dr. Einat Orr is a speaker for ODSC West this October 29th-31st. Be sure to check out her talk, “Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake,” there!
Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data. Join Dr. Einat Orr, at 11:35 on October 30th at ODSC West where she will guide you through that storm, offering a high-level overview of tools and strategies to regain control of your data lake environment. She’ll cover the distinct challenges that come with handling different data types and how modern tools can turn what feels like chaos into a manageable, streamlined architecture.
A Management Layer for Your Data Lake — Diving In
The shift from traditional analytics databases to data lakes came with incredible advantages, but it wasn’t without trade-offs. Data lakes allow for the ingestion of vast amounts of data — regardless of type or format — without the need for a pre-defined schema. This makes them highly scalable, flexible, and perfect for organizations dealing with massive datasets. However, this flexibility often comes at the cost of manageability. The lack of schema enforcement, the complexity of managing ACID guarantees, and the absence of granular access controls are some of the key pain points that data practitioners face when dealing with lakes.
So, how do we bridge the gap between scalability and manageability?
This talk dives into three critical components of data lake management: open table formats, metastores (or catalogs), and data version control systems. Together, these technologies can give you the best of both worlds — the performance and flexibility of a data lake combined with the manageability features of a database. Open table formats, such as Delta Lake or Apache Iceberg, add a crucial metadata layer over raw data files, allowing us to manage schema, enforce transactions, and even track changes to datasets over time. This metadata essentially transforms chaotic collections of files into structured, database-like tables, making data lakes more accessible to SQL-based queries.
Metastores further enhance this manageability by creating a global abstraction layer that standardizes data across the lake, enabling better access control and even bringing back some of the ACID guarantees that traditional databases offer. This gives teams the ability to perform reliable transactions and manage large datasets at scale without sacrificing performance or consistency.
Lastly, data version control systems, like lakeFS, allow for a Git-like approach to managing data. By versioning datasets in the same way we version code, data teams can experiment, roll back changes, and merge data pipelines safely, all without duplicating data or slowing down operations. This is particularly useful in complex environments where teams are constantly iterating on models, performing tests, or handling multiple data streams.
From Theory to Practice
Real-world examples from companies using Databricks, Iceberg, and AWS will illustrate how these technologies can be implemented to achieve a balance between scalability and control. The talk aims to dispel the notion that managing a data lake has to be overwhelming, showing that with the right tools in place, you can maintain order in your data lake without drowning in complexity.
If you’re feeling like you’re treading water in the vast expanse of your data lake, this session will provide practical insights and strategies to help you regain control. By leveraging modern architectures and tooling, you can transform your data lake into a streamlined, well-managed environment, avoiding the common pitfalls that plague data teams everywhere.
About the author/ODSC West 2024 speaker:
Einat Orr has 20+ years of experience building R&D organizations and leading the technology vision at multiple companies, the latest being Similarweb, which made IPO in NYSE last May. Currently, she serves as Co-founder and CEO of Treeverse, the company behind lakeFS, an open-source platform that delivers a git-like experience to object-storage-based data lakes. She received her PhD. in Mathematics from Tel Aviv University, in the field of optimization in graph theory.
