StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Spark vs Kylo

Apache Spark vs Kylo

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
Kylo
Kylo
Stacks15
Followers40
Votes0
GitHub Stars1.1K
Forks571

Apache Spark vs Kylo: What are the differences?

# Apache Spark vs Kylo

Apache Spark and Kylo are both popular big data processing tools, but they have distinct differences that set them apart. Below are the key differences between Apache Spark and Kylo:

1. **Processing Engine**: Apache Spark is a distributed computing system that provides in-memory processing for faster data analysis, while Kylo is a data lake management platform that focuses on simplifying and automating data ingestion, curation, and provisioning.

2. **Use Case Focus**: Apache Spark is best suited for data processing and analytics tasks where speed and performance are crucial, making it ideal for real-time data processing and machine learning applications. Kylo, on the other hand, is designed for managing data lakes and enabling data engineers to efficiently discover, ingest, and curate data for downstream processing.

3. **Scalability**: Apache Spark is known for its scalability, allowing users to seamlessly scale up or down based on the workload requirements. Kylo, although designed for enterprise-scale data lake management, may have limitations in terms of scalability compared to Apache Spark.

4. **Development Flexibility**: Apache Spark provides a rich set of APIs in multiple programming languages like Scala, Java, and Python, offering developers flexibility in writing data processing applications. Kylo, while offering a GUI-driven approach to data ingestion and management, may have fewer options for custom development compared to Apache Spark.

5. **Community and Ecosystem**: Apache Spark has a large and active open-source community with extensive documentation, tutorials, and third-party integrations, making it easier for users to find support and resources. Although Kylo also has a community around it, the ecosystem and community support for Kylo may not be as robust as that of Apache Spark.

6. **Integration with Other Technologies**: Apache Spark is well-integrated with a wide range of big data technologies like Hadoop, Kafka, and Cassandra, making it easier to build end-to-end data pipelines. Kylo, while offering integration with various data sources and processing frameworks, may not have the same level of seamless integration as Apache Spark.

In Summary, Apache Spark excels in processing speed, flexibility, and scalability for data analytics tasks, while Kylo specializes in simplifying data lake management and data ingestion processes for data engineering workflows.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on Apache Spark, Kylo

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Spark
Apache Spark
Kylo
Kylo

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.

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.

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
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
42.2K
GitHub Stars
1.1K
GitHub Forks
28.9K
GitHub Forks
571
Stacks
3.1K
Stacks
15
Followers
3.5K
Followers
40
Votes
140
Votes
0
Pros & Cons
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
Cons
  • 4
    Speed
No community feedback yet
Integrations
No integrations available
ActiveMQ
ActiveMQ
Hadoop
Hadoop
Apache NiFi
Apache NiFi

What are some alternatives to Apache Spark, Kylo?

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.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase