Amundsen vs Delta Lake

Overview

Delta Lake

Stacks105

Followers315

Votes0

GitHub Stars8.4K

Forks1.9K

Amundsen

Stacks17

Followers42

Votes0

Amundsen vs Delta Lake: What are the differences?

Key Differences Between Amundsen and Delta Lake

Amundsen is a metadata driven data discovery and catalog system, while Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Technology Stack: Amundsen is built primarily for metadata management, while Delta Lake focuses on providing ACID transaction capabilities for data lakes.
Use Case: Amundsen is designed for users to explore and discover data assets within an organization, while Delta Lake is focused on ensuring data reliability and consistency in data lakes.
Query Performance: Amundsen does not directly impact query performance, whereas Delta Lake can improve query performance by enabling ACID transactions and optimizing data lake operations.
Integration: Amundsen integrates with various data sources and tools to provide a unified data discovery experience, while Delta Lake integrates with Apache Spark to ensure transactional capabilities for big data workloads.
Data Consistency: Amundsen focuses on metadata consistency and accuracy, whereas Delta Lake emphasizes maintaining transactional consistency for data lake operations.
Infrastructure Support: Amundsen can work with a variety of infrastructure setups, while Delta Lake is specifically optimized for Apache Spark environments.

In Summary, Amundsen is a metadata-driven data discovery and catalog system, while Delta Lake is an open-source storage layer that enables ACID transactions in Apache Spark environments.

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Detailed Comparison

Delta Lake	Amundsen
An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
GitHub Stars 8.4K	GitHub Stars -
GitHub Forks 1.9K	GitHub Forks -
Stacks 105	Stacks 17
Followers 315	Followers 42
Votes 0	Votes 0
Integrations
Apache Spark Hadoop Amazon S3	Google BigQuery Snowflake AWS Glue Superset Apache Hive

What are some alternatives to Delta Lake, Amundsen?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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Amundsen vs Delta Lake: What are the differences?

Key Differences Between Amundsen and Delta Lake

Amundsen is a metadata driven data discovery and catalog system, while Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Technology Stack: Amundsen is built primarily for metadata management, while Delta Lake focuses on providing ACID transaction capabilities for data lakes.
Use Case: Amundsen is designed for users to explore and discover data assets within an organization, while Delta Lake is focused on ensuring data reliability and consistency in data lakes.
Query Performance: Amundsen does not directly impact query performance, whereas Delta Lake can improve query performance by enabling ACID transactions and optimizing data lake operations.
Integration: Amundsen integrates with various data sources and tools to provide a unified data discovery experience, while Delta Lake integrates with Apache Spark to ensure transactional capabilities for big data workloads.
Data Consistency: Amundsen focuses on metadata consistency and accuracy, whereas Delta Lake emphasizes maintaining transactional consistency for data lake operations.
Infrastructure Support: Amundsen can work with a variety of infrastructure setups, while Delta Lake is specifically optimized for Apache Spark environments.

In Summary, Amundsen is a metadata-driven data discovery and catalog system, while Delta Lake is an open-source storage layer that enables ACID transactions in Apache Spark environments.