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

Apache Kylin vs Delta Lake

OverviewComparisonAlternatives

Overview

Apache Kylin
Apache Kylin
Stacks61
Followers236
Votes24
GitHub Stars3.8K
Forks1.5K
Delta Lake
Delta Lake
Stacks105
Followers315
Votes0
GitHub Stars8.4K
Forks1.9K

Apache Kylin vs Delta Lake: 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, Delta Lake is detailed as "Reliable Data Lakes at Scale". An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

Apache Kylin and Delta Lake can be categorized as "Big Data" tools.

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, Delta Lake provides the following key features:

  • ACID Transactions
  • Scalable Metadata Handling
  • Time Travel (data versioning)

Apache Kylin and Delta Lake are both open source tools. It seems that Apache Kylin with 2.23K GitHub stars and 992 forks on GitHub has more adoption than Delta Lake with 1.26K GitHub stars and 210 GitHub forks.

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Detailed Comparison

Apache Kylin
Apache Kylin
Delta Lake
Delta Lake

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.

An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API
Statistics
GitHub Stars
3.8K
GitHub Stars
8.4K
GitHub Forks
1.5K
GitHub Forks
1.9K
Stacks
61
Stacks
105
Followers
236
Followers
315
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
Apache Spark
Apache Spark
Hadoop
Hadoop
Amazon S3
Amazon S3

What are some alternatives to Apache Kylin, Delta Lake?

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.

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.

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