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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Kylin vs Google Cloud Data Fusion

Apache Kylin vs Google Cloud Data Fusion

OverviewComparisonAlternatives

Overview

Apache Kylin
Apache Kylin
Stacks61
Followers236
Votes24
GitHub Stars3.8K
Forks1.5K
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Apache Kylin vs Google Cloud Data Fusion: What are the differences?

Introduction

Apache Kylin and Google Cloud Data Fusion are two popular big data processing platforms used for analytics and data processing tasks. While both offer powerful features, there are key differences between the two that can influence the choice of platform for specific use cases.

  1. Architecture: Apache Kylin is an open-source distributed analytics engine designed to provide OLAP (Online Analytical Processing) capability on big data. It stores pre-aggregated data cubes to accelerate query performance. On the other hand, Google Cloud Data Fusion is a fully-managed data integration service that allows users to efficiently build and manage ETL (Extract, Transform, Load) pipelines without worrying about infrastructure management.

  2. Scalability: Apache Kylin is horizontally scalable, meaning it can handle large volumes of data by adding more nodes to the cluster. Google Cloud Data Fusion, being a managed service on Google Cloud Platform, offers automatic scaling based on workload demands, allowing for seamless scalability without user intervention.

  3. Data Sources: Apache Kylin primarily works with Hadoop-based data sources such as HDFS (Hadoop Distributed File System) and HBase. In contrast, Google Cloud Data Fusion supports a wide range of data sources including Google Cloud Storage, BigQuery, Cloud SQL, and more, making it versatile for organizations using Google Cloud Platform services.

  4. Pricing Model: Apache Kylin is an open-source project, which means it is free to use with community support. Google Cloud Data Fusion follows a pay-as-you-go pricing model based on data processed and transformations executed. Users pay for the resources consumed during data integration tasks.

  5. Ecosystem Integration: Apache Kylin integrates well with the Apache Hadoop ecosystem, including tools like Spark, Hive, and Kafka. On the other hand, Google Cloud Data Fusion seamlessly integrates with other Google Cloud services such as BigQuery, Dataflow, and Dataproc, providing a cohesive data processing environment within the Google Cloud Platform.

In Summary, Apache Kylin and Google Cloud Data Fusion offer different approaches to big data processing, with Apache Kylin focusing on OLAP capabilities and Apache Hadoop ecosystem integration, while Google Cloud Data Fusion provides a managed service for ETL pipelines with seamless scalability and integration with Google Cloud services.

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Detailed Comparison

Apache Kylin
Apache Kylin
Google Cloud Data Fusion
Google Cloud Data Fusion

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.

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

Extremely Fast OLAP Engine at Scale; ANSI SQL Interface on Hadoop; Interactive Query Capability; MOLAP Cube; Seamless Integration with BI Tools
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
GitHub Stars
3.8K
GitHub Stars
-
GitHub Forks
1.5K
GitHub Forks
-
Stacks
61
Stacks
25
Followers
236
Followers
156
Votes
24
Votes
1
Pros & Cons
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
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Hadoop
Hadoop
Apache Spark
Apache Spark
Tableau
Tableau
PowerBI
PowerBI
Superset
Superset
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to Apache Kylin, Google Cloud Data Fusion?

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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