Dremio vs Amundsen

Overview

Dremio

Stacks116

Followers348

Votes8

Amundsen

Stacks17

Followers42

Votes0

Dremio vs Amundsen: What are the differences?

Developers describe Dremio as "Self-service data for everyone". It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others. 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.

Dremio and Amundsen belong to "Big Data Tools" category of the tech stack.

Some of the features offered by Dremio are:

Democratize all your data
Make your data engineers more productive
Accelerate your favorite tools

On the other hand, Amundsen provides the following key features:

Datasets (Tables) schema and usage frequency/popularity
Users bookmark, owner, frequent user
Dashboard popularity, lineage to datasets

Amundsen is an open source tool with 889 GitHub stars and 163 GitHub forks. Here's a link to Amundsen's open source repository on GitHub.

🔥 Trending in Databases on StackShare

CrateDB Databases

Real Time Analytics Database

Try it View Docs Alternatives

Try

0

1

Advice on Dremio, Amundsen

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Detailed Comparison

Dremio	Amundsen
Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
Stacks 116	Stacks 17
Followers 348	Followers 42
Votes 8	Votes 0
Pros & Cons
Pros 3 Nice GUI to enable more people to work with Data 2 Easier to Deploy 2 Connect NoSQL databases with RDBMS 1 Free Cons 1 Works only on Iceberg structured data	No community feedback yet
Integrations
Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI	Google BigQuery Snowflake AWS Glue Superset Apache Hive

What are some alternatives to Dremio, Amundsen?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.