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  1. Stackups
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  5. Azure Data Factory vs Kylo

Azure Data Factory vs Kylo

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
Kylo
Kylo
Stacks15
Followers40
Votes0
GitHub Stars1.1K
Forks571

Azure Data Factory vs Kylo: What are the differences?

Azure Data Factory: 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; Kylo: Open-source data lake management software platform. 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.

Azure Data Factory can be classified as a tool in the "Integration Tools" category, while Kylo is grouped under "Big Data Tools".

Some of the features offered by Azure Data Factory are:

  • Real-Time Integration
  • Parallel Processing
  • Data Chunker

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

  • 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

Azure Data Factory and Kylo are both open source tools. Kylo with 744 GitHub stars and 358 forks on GitHub appears to be more popular than Azure Data Factory with 155 GitHub stars and 256 GitHub forks.

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Advice on Azure Data Factory, Kylo

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
Kylo
Kylo

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 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.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
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
516
GitHub Stars
1.1K
GitHub Forks
610
GitHub Forks
571
Stacks
253
Stacks
15
Followers
484
Followers
40
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
ActiveMQ
ActiveMQ
Apache Spark
Apache Spark
Hadoop
Hadoop
Apache NiFi
Apache NiFi

What are some alternatives to Azure Data Factory, Kylo?

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.

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.

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.

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