Frequently Asked Questions About the Data Lakehouse
Question Index What is a Data Lakehouse? How is a Data Lakehouse different from a Data Warehouse? How is the Data Lakehouse different from a Data Lake? How easy is it for data analysts to use a Data Lakehouse? How do Data Lakehouse systems compare in performance and cost to data warehouses? What data governance...
Announcing the Launch of Delta Live Tables: Reliable Data Engineering Made Easy
As the amount of data, data sources and data types at organizations grow, building and maintaining reliable data pipelines has become a key enabler for analytics, data science and machine learning (ML). Prioritizing these initiatives puts increasing pressure on data engineering teams because processing the raw, messy data into clean, fresh, reliable data is a...
Introducing Delta Sharing: An Open Protocol for Secure Data Sharing
Data sharing has become critical in the modern economy as enterprises look to securely exchange data with their customers, suppliers and partners. For example, a retailer may want to publish sales data to its suppliers in real time, or a supplier may want to share real-time inventory. But so far, data sharing has been severely...
What is a Lakehouse?
Over the past few years at Databricks, we've seen a new data management architecture that emerged independently across many customers and use cases: the lakehouse. In this post we describe this new architecture and its advantages over previous approaches. Data warehouses have a long history in decision support and business intelligence applications. Since its inception...
Delta Lake Now Hosted by the Linux Foundation to Become the Open Standard for Data Lakes
At today’s Spark + AI Summit Europe in Amsterdam, we announced that Delta Lake is becoming a Linux Foundation project. Together with the community, the project aims to establish an open standard for managing large amounts of data in data lakes. The Apache 2.0 software license remains unchanged. Delta Lake focuses on improving the reliability...
Diving Into Delta Lake: Unpacking The Transaction Log
The transaction log is key to understanding Delta Lake because it is the common thread that runs through many of its most important features, including ACID transactions, scalable metadata handling, time travel, and more. In this article, we’ll explore what the Delta Lake transaction log is, how it works at the file level, and how...
How to Work with Avro, Kafka, and Schema Registry in Databricks
In the previous blog post, we introduced the new built-in Apache Avro data source in Apache Spark and explained how you can use it to build streaming data pipelines with the from_avro and to_avro functions. Apache Kafka and Apache Avro are commonly used to build a scalable and near-real-time data pipeline. In this blog post,...
Apache Avro as a Built-in Data Source in Apache Spark 2.4
Apache Avro is a popular data serialization format. It is widely used in the Apache Spark and Apache Hadoop ecosystem, especially for Kafka-based data pipelines. Starting from Apache Spark 2.4 release, Spark provides built-in support for reading and writing Avro data. The new built-in spark-avro module is originally from Databricks’ open source project Avro Data...
How to Avoid Drowning in GDPR Data Subject Requests in a Data Lake
With GDPR enforcement rapidly approaching (May 25, 2018), many companies are still trying to figure out how to comply. A big pain point, particularly for companies who utilize data lakes to store vast amounts of data, is how to comply with one of the main requirements under the GDPR - data subject requests, also known...
Introducing Low-latency Continuous Processing Mode in Structured Streaming in Apache Spark 2.3
Structured Streaming in Apache Spark 2.0 decoupled micro-batch processing from its high-level APIs for a couple of reasons. First, it made developer’s experience with the APIs simpler: the APIs did not have to account for micro-batches. Second, it allowed developers to treat a stream as an infinite table to which they could issue queries as...
