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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Kylo vs Zato

Kylo vs Zato

OverviewComparisonAlternatives

Overview

Zato
Zato
Stacks12
Followers24
Votes0
GitHub Stars988
Forks246
Kylo
Kylo
Stacks15
Followers40
Votes0
GitHub Stars1.1K
Forks571

Zato vs Kylo: What are the differences?

Developers describe Zato as "Open-source ESB, SOA, REST and Cloud Integrations in Python". Build and orchestrate integration services, expose new or existing APIs, either cloud or on-premise, and use a wide range of connectors, data formats and protocols. On the other hand, Kylo is detailed as "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.

Zato and Kylo can be primarily classified as "Big Data" tools.

Some of the features offered by Zato are:

  • Highly scalable enterprise integration platform and backend application server in Python
  • Browser-based GUI, CLI and API - designed by pragmatists for pragmatists
  • Protocols, industry standards and data formats - Odoo, SAP, IBM MQ, REST, Publish/Subscribe Queues, Single Sign-On, AMQP, SOAP, SQL, NoSQL, Caching, Kafka, WebSockets, LDAP, ElasticSearch, SMS, ZeroMQ, RBAC, Cassandra, S3, JMS and more

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

Zato and Kylo are both open source tools. Zato with 796 GitHub stars and 185 forks on GitHub appears to be more popular than Kylo with 744 GitHub stars and 358 GitHub forks.

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Detailed Comparison

Zato
Zato
Kylo
Kylo

Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python. ESB, SOA, REST, API and Cloud Integrations in Python.

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.

Integrate everything. In Python.; Connect, integrate and automate all of your systems, APIs and apps, including cloud and legacy ones, using an open-source integration platform in Python.;Say goodbye to integration challenges and hello to peace of mind.
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
988
GitHub Stars
1.1K
GitHub Forks
246
GitHub Forks
571
Stacks
12
Stacks
15
Followers
24
Followers
40
Votes
0
Votes
0
Integrations
Docker
Docker
MySQL
MySQL
Linux
Linux
MSSQL
MSSQL
Microsoft Azure
Microsoft Azure
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Odoo
Odoo
Ubuntu
Ubuntu
SQL
SQL
ActiveMQ
ActiveMQ
Apache Spark
Apache Spark
Hadoop
Hadoop
Apache NiFi
Apache NiFi

What are some alternatives to Zato, 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.

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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