Datameer vs Kylo
Overview
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CLI (Node.js)
Manual
Detailed Comparison
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. | It is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects. |
Data integration;
Data visualization;
Dynamic data management;
Open infrastructure;
Pre-built application;
Self-service analytics. | Self-service data ingest with data cleansing, validation, and automatic profiling; Wrangle data with visual sql and an interactive transform through a simple user interface; Search and explore data and metadata, view lineage, and profile statistics; Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance |
Statistics | |
GitHub Stars - | GitHub Stars 1.1K |
GitHub Forks - | GitHub Forks 571 |
Stacks 5 | Stacks 15 |
Followers 12 | Followers 40 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Datameer, Kylo?
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
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.
Superset
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.
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.
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
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.
