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  5. Datameer vs Superset

Datameer vs Superset

OverviewComparisonAlternatives

Overview

Superset
Superset
Stacks420
Followers1.0K
Votes45
Datameer
Datameer
Stacks5
Followers12
Votes0

Datameer vs Superset: What are the differences?

Markdown code:

<Write Introduction here>
1. **Key Difference 1**: Datameer is designed for business users without the need for programming skills, while Superset is more suitable for data analysts and SQL developers.
2. **Key Difference 2**: Datameer provides a unified interface for data integration, transformation, and analysis, whereas Superset primarily focuses on data visualization and exploration.
3. **Key Difference 3**: Datameer offers pre-built data connectors and data preparation tools to streamline the data processing workflow, whereas Superset relies on external data sources and does not provide data transformation capabilities.
4. **Key Difference 4**: Datameer is a commercial software with a licensing fee, while Superset is an open-source project supported by a community of developers.
5. **Key Difference 5**: Datameer includes advanced analytics features like predictive modeling and machine learning algorithms, whereas Superset is more focused on traditional business intelligence functionalities.
6. **Key Difference 6**: Datameer supports collaborative workflows through shared datasets and workflow automation, whereas Superset primarily serves as a standalone data visualization tool without extensive collaboration features.

In Summary, Datameer and Superset differ in their target user base, functionality focus, pricing model, advanced analytics capabilities, collaborative features, and customization options.

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Detailed Comparison

Superset
Superset
Datameer
Datameer

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

It is a single application that helps you get any data into Hadoop, bring it together, analyze it, and visualize it as quickly and easily as possible. No coding required. Everything in it is self-service and intuitive, from our wizard-based data integration, to a spreadsheet with point-and-click analytics, to our blank canvas to for building custom visualizations.

A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities;An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler);A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user;Deep integration with Druid allows for Caravel to stay blazing fast while slicing and dicing large, realtime datasets;
Data integration; Data visualization; Dynamic data management; Open infrastructure; Pre-built application; Self-service analytics.
Statistics
Stacks
420
Stacks
5
Followers
1.0K
Followers
12
Votes
45
Votes
0
Pros & Cons
Pros
  • 13
    Awesome interactive filtering
  • 9
    Free
  • 6
    Wide SQL database support
  • 6
    Shareable & editable dashboards
  • 5
    Great for data collaborating on data exploration
Cons
  • 4
    Link diff db together "Data Modeling "
  • 3
    It is difficult to install on the server
  • 3
    Ugly GUI
No community feedback yet
Integrations
No integrations available
Amazon S3
Amazon S3
Microsoft Azure
Microsoft Azure
MySQL
MySQL
Oracle
Oracle
PostgreSQL
PostgreSQL
Beehive
Beehive
Snowflake
Snowflake

What are some alternatives to Superset, Datameer?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Apache Kylin

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.

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