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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Kylin vs Grooper

Apache Kylin vs Grooper

OverviewComparisonAlternatives

Overview

Apache Kylin
Apache Kylin
Stacks61
Followers236
Votes24
GitHub Stars3.8K
Forks1.5K
Grooper
Grooper
Stacks1
Followers2
Votes0

Apache Kylin vs Grooper: What are the differences?

Developers describe Apache Kylin as "OLAP Engine for Big Data". 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. On the other hand, Grooper is detailed as "Innovate workflows by integrating difficult data". It empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, It is an intelligent document and digital data integration platform. It combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing.

Apache Kylin and Grooper are primarily classified as "Big Data" and "Data Science" tools respectively.

Some of the features offered by Apache Kylin are:

  • Extremely Fast OLAP Engine at Scale
  • ANSI SQL Interface on Hadoop
  • Interactive Query Capability

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

  • Text and Document Classification
  • Hierarchical Data Modeling
  • Image Capture

Apache Kylin is an open source tool with 2.62K GitHub stars and 1.18K GitHub forks. Here's a link to Apache Kylin's open source repository on GitHub.

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Detailed Comparison

Apache Kylin
Apache Kylin
Grooper
Grooper

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.

It empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, It is an intelligent document and digital data integration platform. It combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing.

Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Text and Document Classification; Hierarchical Data Modeling; Image Capture; Electronic Document Handling; Image Processing and Computer Vision; OCR; Data Extraction; Machine Learning; Natural Language Processing; Fuzzy Data Handling; Data Output; Document Rendering
Statistics
GitHub Stars
3.8K
GitHub Stars
-
GitHub Forks
1.5K
GitHub Forks
-
Stacks
61
Stacks
1
Followers
236
Followers
2
Votes
24
Votes
0
Pros & Cons
Pros
  • 7
    Star schema and snowflake schema support
  • 5
    Seamless BI integration
  • 4
    OLAP on Hadoop
  • 3
    Easy install
  • 3
    Sub-second latency on extreme large dataset
No community feedback yet
Integrations
Hadoop
Hadoop
Apache Spark
Apache Spark
Tableau
Tableau
PowerBI
PowerBI
Superset
Superset
Alfresco
Alfresco
OneDrive
OneDrive
Microsoft SharePoint
Microsoft SharePoint

What are some alternatives to Apache Kylin, Grooper?

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.

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Splunk

Splunk

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

Apache Impala

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

Vertica

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

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