Amundsen vs AresDB

Overview

AresDB

Stacks15

Followers47

Votes0

GitHub Stars3.1K

Forks235

Amundsen

Stacks17

Followers42

Votes0

AresDB vs Amundsen: What are the differences?

Developers describe AresDB as "A GPU-powered real-time analytics storage and query engine (by Uber)". AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management. On the other hand, Amundsen is detailed as "A metadata driven application for improving the productivity of data analysts, data scientists and engineers". It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

AresDB and Amundsen can be primarily classified as "Big Data" tools.

AresDB and Amundsen are both open source tools. It seems that AresDB with 2.34K GitHub stars and 171 forks on GitHub has more adoption than Amundsen with 889 GitHub stars and 163 GitHub forks.

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CLI (Node.js)

Manual

Detailed Comparison

AresDB	Amundsen
AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
-	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
GitHub Stars 3.1K	GitHub Stars -
GitHub Forks 235	GitHub Forks -
Stacks 15	Stacks 17
Followers 47	Followers 42
Votes 0	Votes 0
Integrations
No integrations available	Google BigQuery Snowflake AWS Glue Superset Apache Hive

What are some alternatives to AresDB, 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.