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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Azure Data Factory vs Grooper

Azure Data Factory vs Grooper

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
Grooper
Grooper
Stacks1
Followers2
Votes0

Azure Data Factory vs Grooper: What are the differences?

Developers describe Azure Data Factory as "Hybrid data integration service that simplifies ETL at scale". It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud. 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.

Azure Data Factory and Grooper are primarily classified as "Big Data" and "Data Science" tools respectively.

Some of the features offered by Azure Data Factory are:

  • Real-Time Integration
  • Parallel Processing
  • Data Chunker

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

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

Azure Data Factory is an open source tool with 175 GitHub stars and 278 GitHub forks. Here's a link to Azure Data Factory's open source repository on GitHub.

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Advice on Azure Data Factory, Grooper

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
Grooper
Grooper

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
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
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
1
Followers
484
Followers
2
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
Alfresco
Alfresco
OneDrive
OneDrive
Microsoft SharePoint
Microsoft SharePoint

What are some alternatives to Azure Data Factory, 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.

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.

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.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

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

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