Understanding Data Lake Flexibility and Its Challenges

Data lakes went from the answer to the data bottleneck to data swamps at many companies I have talked to. When I first broke into the data world, everyone wanted to build a data lake. They thought it was the key to letting data scientists and ML engineers deliver value quickly. Schema-on-read was hailed as a revolutionary idea! But you don’t hear too many people talking about it anymore. Data teams quickly found that many data lakes still required some level of pre-processing. To make matters worse it wasn’t uncommon to find data workflows that had thousands of lines of SQL queries and Python scripts that were untested and hard to trace all to calculate a few metrics. I recall having to help a few friends look through script after script trying to figure out where their small change request would actually need to go because of how complex some workflows were. This isn’t to say that data lakes don’t have their place. I have seen them used successfully at companies where they often used it as a layer to develop MVPs of ML models prior to implementing a more reliable process to take the data from the data lake into a more standard data storage solution. But in these cases, there was generally a clear process and some level of governance(both in terms of data and code). To many degrees data lakes and data lakehouses were all developed around technologies and vendors(data lakes - Hadoop, data lakehouses Databricks), and whether you think they are right or wrong is less of the point. You need to get past the marketing and figure out what processes make your implementation successful otherwise we’ll just keep going through the same cycle every decade or so. But I'd love to hear your thoughts, how can companies successfully create processes that build reliable data systems and teams.

🚀 The Evolution of Data Warehousing: From ETL to Lakehouse The data warehousing landscape has undergone a massive #transformation over the past few decades — driven by growing data volumes, the demand for agility, and the need for faster, more reliable insights. 🏛️ The Birth of the Enterprise Data Warehouse (EDW) 35–40 years ago, the Enterprise Data Warehouse (EDW) emerged as a centralized repository for reporting and analytics. * Data was integrated from multiple operational systems via #ETL (Extract → Transform → Load). * Tables were predefined, and transformations happened before loading — a #schema-on-write approach. * Reporting tools relied on consistent, structured, relational data. * This model prioritized #governance, #quality, and #reliability, but struggled with flexibility and scalability. 🌊 The Rise of the Data Lake About 15 years ago, the Data Lake emerged — first via Hadoop Distributed File System (#HDFS) and later through cloud-native object storage like #Amazon S3 and Azure Data Lake Storage (#ADLS). This era introduced two key shifts: * #ELT (Extract → Load → Transform) replaced traditional ETL, allowing more flexibility by performing transformations post-load. * A #schema-on-read approach enabled storing raw, #unstructured, or semi-structured data without enforcing a schema upfront. 🔻 Limitations of Classic Data Lakes Despite their flexibility and scalability, traditional data lakes had critical shortcomings: ❌ Lack of schema enforcement – Made it harder to manage and validate data. ❌ No ACID guarantees – Data consistency was not ensured in concurrent environments. ❌ No transactional consistency – No safe way to update or delete data without risks. As a result, data lakes were often unsuitable for BI, governance, or regulatory use cases. ☁️ The #Cloud #Data #Warehouse Era (2012- Present) To address the limitations of both EDWs and classic data lakes, cloud data warehouses emerged. They brought scalability, performance, and accessibility by leveraging cloud infrastructure. Key platforms include: * Snowflake * Google BigQuery * Azure Synapse Analytics * Amazon Redshift Key benefits: * Fully managed infrastructure * High performance and concurrency * Familiar #SQL interfaces However, these systems still had limitations, including closed formats, vendor lock-in, and cost challenges at extreme scale. 🏠 The Data Lakehouse: The Best of Both Worlds (2019 - Present) The Lakehouse architecture emerged as a hybrid solution, combining the cost-efficiency and flexibility of data lakes with the structure and reliability of data warehouses. Key components: * Open table formats like Apache Iceberg and Delta Lake * Open, scalable storage (e.g., S3, ADLS) * ACID transactions directly on the data lake * Query engines like #Presto, Trino, #Spark SQL, and Athena enable #SQL queries directly on lake data This unified architecture allows organizations to support #BI, data #engineering, #datascience, and #ML.

Snowflake was not an innovation and neither is most of the tech we use today. Technology is a pendulum. With every swing we reimagine existing tech and approaches. We apply new thinking and methods for the purpose of optimizing 3 primary aspects: 1. Costs 2. Scale & performance 3. Utility Rarely do we see truly breakthrough innovation that changes how we do things. The data and analytics industry is no different. Data warehouses were created by optimizing how relational databases store, retrieve and process large amounts of data. Cloud-native warehouses took the traditional warehouse design and deployed it on cloud-hosted compute resources, improving performance and reducing compute costs. Soon after they realized that separating compute from storage reduces costs further and increases scalability. They took learnings from engineers solving big data problems with distributed processing engines like Hadoop. That was Snowflake's original contribution. Combined with smart UI decisions made the cloud-native warehouse simple to use, scalable and relatively cost-effective, and super popular. Data lakes emerged sort of in parallel to the development of cloud-native warehouses. Starting with Hadoop and HDFS based lakes and evolving into Spark and object store (S3, GCP, ADLS) based lakes that could scale higher and more economically. Additionally, the cloud-native lakes also decoupled the metadata layer, or catalog, that made it easier for query engines to find and access data separately from the engines that created it. Both cloud-native warehouses and lakes took many lessons from each other and continued to optimize, you know it, costs, performance/scale and utility. Today we're starting to see the technology pendulum swinging back from the highs of cloud-native warehouses, back towards the data lake. We learned what is needed to make data lakes successful - easier to use, performant and more utilitarian - useful in more than one use case. Lakehouses is how we make data lakes better. Lakehosues built on open table formats like #ApacheIceberg are not revolutionary or overly innovative. They simply decouple key components to improve scale, performance and enable a rich ecosystem of tools. Lakehouses decouple: - Query processing (compute) - Metadata/catalog - Transaction management - Table services (optimizations, cleanup, compaction, etc.) - Storage using open file formats Lakehouses aren't unique. They are simply reinventing the data lake in ways that make your data more accessible, easier to use, and offers greater utility of your data. ------ Want to hear more, check out the recording from my latest session comparing data lakes with Lakehouses. Link in comments. ------ I'm also hosting an in-person conference in NYC on Feb 6 where speakers dive much deeper into all of this. Come join us, registration link in the comments. #dataengineering