3/8/2023 0 Comments Databricks lakehouse![]() The company claims that it “streamlines ML development, from data preparation to model training and deployment, at scale.” MLflow is an open source framework that Databricks released to help with this. Machine learning is another key part of Databricks’ offering. And then we provide you with this optimized format, an optimized query engine, and transactional guarantees for querying that data for all kinds of use cases and applications.” “It basically gives you similar capabilities of a data warehouse, on top of a data lake,” he continued, “and that’s why the way to think about Databricks is, we are database agnostic you can ingest data into Databricks and into a delta lake, from any data source. “A couple of years ago we built a product called Delta Lake,” Mewald told me, describing it as “both a storage format and a transaction layer.” It aims to ensure the reliability of data across data lakes at a massive scale the technology was open sourced last April. While “the lakehouse” might be contentious, Databricks does at least have a product that actually implements the theory: Delta Lake. Business Intelligence analyst Barry Devlin wrote in response to the Databricks post that “while often claimed to be an architecture, the data lake has never really matured beyond a marketing concept.” He wonders, “can the lakehouse do better?” It should be noted that, unlike data warehouses, the data lake concept has not been universally accepted in the industry. Image via Databricks (click to view large version) It was described as “a new paradigm that combines the best elements of data lakes and data warehouses.” In a blog post from January, Databricks extended the data lake idea by coining a new term: the lakehouse. A key difference: data lakes were designed to deal with the internet and its masses of unstructured data. The term was coined in 2011, as a modern variation of the late-1980s concept of a data warehouse. He replied that Databricks is “database agnostic.” The company specializes in large scale data processing, he said, but the real key to its approach is the data lake theory.Ī data lake is a repository of raw data stored in a variety of formats - anything from unstructured data like emails and PDFs, to structured data from a relational database. I started by asking Mewald how Databricks relates to modern database systems, such as Apache Cassandra and MongoDB? Mewald has an especially interesting background when it comes to AI data, having worked for four years on the Google Brain team building ML infrastructure for Google. To find out more about Databricks’ strategy in the age of AI, I spoke with Clemens Mewald, the company’s director of product management, data science and machine learning. Previously he founded ReadWriteWeb in 2003 and built it into one of the world’s most influential technology news and analysis sites. It also defines three must-have elements-multi-node streaming, auto-scaling, and heterogeneous platform support-that enable CDC to meet modern enterprise requirements.Richard is senior editor at The New Stack and writes a weekly column about what's next on the cloud native internet. This webinar explains what CDC is, why enterprises need it, and how to design CDC pipelines for ingestion into the Databricks Lakehouse. CDC continuously transfers new and changed data into Databricks, which increases efficiency, scalability, and performance compared with legacy batch pipelines. Streaming data pipelines, enabled by real-time change data capture (CDC), can help. But to achieve this, the Databricks Lakehouse needs to ingest high volumes of data from operational and analytical databases and data warehouses at low latency. The Databricks Lakehouse offers enterprises the opportunity to consolidate BI and data science workloads onto the same platform, combining the performance and governance of the data warehouse with the flexibility of the data lake.
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