Apache Kylin vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Kylin

Stacks61

Followers236

Votes24

GitHub Stars3.8K

Forks1.5K

Apache Kylin vs Apache Spark: What are the differences?

Introduction

Apache Kylin and Apache Spark are both powerful open-source big data processing frameworks. While they share some similarities, they also have several key differences that set them apart.

Data Processing Paradigm: Apache Kylin is specifically designed for online analytical processing (OLAP) and provides advanced query capabilities on large datasets. It leverages pre-built data cubes to provide fast query performance. On the other hand, Apache Spark is a general-purpose distributed computing framework that supports a wide range of data processing tasks, including batch processing, real-time streaming, machine learning, and graph processing.
Data Modeling Approach: Apache Kylin uses a multidimensional model called the star schema for data modeling. It requires denormalized, pre-aggregated data to build cubes and achieve fast query performance. Apache Spark, on the other hand, supports various data modeling approaches, including relational models, graph models, and document models. It can process both raw and structured data, allowing for more flexibility in data modeling.
Query Performance: Apache Kylin is optimized for query performance on large datasets. By leveraging pre-built cubes and using techniques like bitmap indexing and storage optimization, it provides sub-second query responses on billions of rows of data. Apache Spark, on the other hand, provides high-performance distributed data processing but may not have the same level of query optimization as Kylin. It is more suitable for complex data processing tasks that involve transformations, aggregations, and analytics.
Scalability: Apache Kylin is designed for horizontal scalability and can handle large datasets by distributing and parallelizing query processing. It supports automatic cube building and incremental cube updates for efficient data processing. Apache Spark is also highly scalable and can handle big data workloads by distributing data and computation across a cluster of machines. It offers fault-tolerance, data replication, and dynamic resource allocation for scalability.
Real-time Processing: While Apache Kylin focuses on batch processing and OLAP, Apache Spark provides real-time streaming capabilities through its Spark Streaming module. It allows processing of real-time data streams and supports windowed aggregations, event-time processing, and integration with various messaging systems. Kylin, however, does not have native support for real-time streaming and is primarily suited for batch processing.
Ecosystem and Integration: Apache Spark has a vast ecosystem of libraries and integrations, making it a popular choice for big data processing. It supports integration with various data sources, analytics tools, machine learning frameworks, and cloud platforms. Apache Kylin, on the other hand, is more specialized for OLAP and has limited integrations with other frameworks. It primarily focuses on providing efficient query performance rather than a broad ecosystem.

In summary, Apache Kylin is optimized for OLAP and provides fast query performance through pre-built cubes and a specialized data modeling approach. Apache Spark, on the other hand, is a general-purpose big data processing framework with support for various data processing tasks, scalability, real-time streaming, and a vast ecosystem of libraries and integrations.

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Advice on Apache Spark, Apache Kylin

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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.

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Detailed Comparison

Apache Spark	Apache Kylin
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.	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.
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	Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Statistics
GitHub Stars 42.2K	GitHub Stars 3.8K
GitHub Forks 28.9K	GitHub Forks 1.5K
Stacks 3.1K	Stacks 61
Followers 3.5K	Followers 236
Votes 140	Votes 24
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed	Pros 7 Star schema and snowflake schema support 5 Seamless BI integration 4 OLAP on Hadoop 3 Sub-second latency on extreme large dataset 3 Easy install
Integrations
No integrations available	Hadoop Tableau PowerBI Superset

What are some alternatives to Apache Spark, Apache Kylin?

Presto

Distributed SQL Query Engine for Big Data

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 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

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 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

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

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

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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.

Apache Kudu

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Related Comparisons

Apache Kylin vs Apache Spark: What are the differences?

Introduction

Apache Kylin and Apache Spark are both powerful open-source big data processing frameworks. While they share some similarities, they also have several key differences that set them apart.

Data Processing Paradigm: Apache Kylin is specifically designed for online analytical processing (OLAP) and provides advanced query capabilities on large datasets. It leverages pre-built data cubes to provide fast query performance. On the other hand, Apache Spark is a general-purpose distributed computing framework that supports a wide range of data processing tasks, including batch processing, real-time streaming, machine learning, and graph processing.
Data Modeling Approach: Apache Kylin uses a multidimensional model called the star schema for data modeling. It requires denormalized, pre-aggregated data to build cubes and achieve fast query performance. Apache Spark, on the other hand, supports various data modeling approaches, including relational models, graph models, and document models. It can process both raw and structured data, allowing for more flexibility in data modeling.
Query Performance: Apache Kylin is optimized for query performance on large datasets. By leveraging pre-built cubes and using techniques like bitmap indexing and storage optimization, it provides sub-second query responses on billions of rows of data. Apache Spark, on the other hand, provides high-performance distributed data processing but may not have the same level of query optimization as Kylin. It is more suitable for complex data processing tasks that involve transformations, aggregations, and analytics.
Scalability: Apache Kylin is designed for horizontal scalability and can handle large datasets by distributing and parallelizing query processing. It supports automatic cube building and incremental cube updates for efficient data processing. Apache Spark is also highly scalable and can handle big data workloads by distributing data and computation across a cluster of machines. It offers fault-tolerance, data replication, and dynamic resource allocation for scalability.
Real-time Processing: While Apache Kylin focuses on batch processing and OLAP, Apache Spark provides real-time streaming capabilities through its Spark Streaming module. It allows processing of real-time data streams and supports windowed aggregations, event-time processing, and integration with various messaging systems. Kylin, however, does not have native support for real-time streaming and is primarily suited for batch processing.
Ecosystem and Integration: Apache Spark has a vast ecosystem of libraries and integrations, making it a popular choice for big data processing. It supports integration with various data sources, analytics tools, machine learning frameworks, and cloud platforms. Apache Kylin, on the other hand, is more specialized for OLAP and has limited integrations with other frameworks. It primarily focuses on providing efficient query performance rather than a broad ecosystem.

Apache Kylin vs Apache Spark

Overview