This evolution has been led

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Like it or not, data lakes continue to populate today. They allow data engineers to “save now” and decide what to do with the data later. However, the basic data lake architecture has been extended to include more advanced data discovery and management capabilities.

by in-house solutions and stellar startups like Databricks and Snowflake, but many more are in development. Their various architectures are now under scrutiny by data center designers looking for new opportunities in AI.

Data Lake Evolution: From Lake to Lakehouse
Among the solutions participating in the data lake game are Amazon Lake Formation, Cloudera Open Data Lakehouse, Dell Data Lakehouse, Dremio Lakehouse platform, Google BigLake, IBM watsonx.data, Microsoft Azure Data Lake Storage, Oracle Cloud Infrastructure, Scality Ring and Starburst Galaxy.

As you can see from this list, the trend is to call offerings “lakehouses” rather than data lakes. The name suggests something closer to traditional data warehouses designed to handle structured data. And, yes, this is another strained analogy that, like the data lake before it, has come under scrutiny.

In the data world, naming is an art. Today, systems benin mobile database address the initial shortcomings of the data lake are called integrated data platforms, hybrid data management solutions, and so on. But weird naming conventions shouldn’t obscure important advances in functionality.

In modern, updated analytics platforms, various data processing components are connected in a pipeline. Advances in the creation of a new data factory can be associated with:

New table formats: For example, Delta Lake and Iceberg, built on top of cloud object storage, provide ACID transaction support for Apache Spark, Hadoop, and other data processing systems. The widely used Parquet format can help optimize data compression.
Metadata catalogs: Tools like Snowflake Data Catalog and Databricks Unify Catalog are just a few of the tools that search for data and track its lineage. The latter is important for ensuring data quality for analytics.
Query engines. These provide a common SQL interface for high-performance querying of a wide variety of data types stored in a wide variety of locations. Examples include PrestoDB, Trinio, and Apache Spark.
These improvements collectively reflect today's efforts to make data analysis more organized, efficient, and easier to manage.

They are accompanied by a significant shift to “load now, transform later” methods. This is a change from the usual sequence of data processing in a data warehouse – “extract, transform, load” (ETL). Now the recipe can be “extract, load, transform” (ELT) instead.

Whatever you call it, this is a defining moment for advanced data architectures. They arrived just in time for the shiny new developments in generative AI. But their evolution from “clutter” to something more clearly defined has been slow.