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

EventQL vs Google Cloud Data Fusion

OverviewComparisonAlternatives

Overview

EventQL
EventQL
Stacks3
Followers9
Votes3
GitHub Stars1.2K
Forks104
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

EventQL vs Google Cloud Data Fusion: What are the differences?

Developers describe EventQL as "The database for large-scale event analytics". EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries. On the other hand, Google Cloud Data Fusion is detailed as "Fully managed, code-free data integration at any scale". 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.

EventQL and Google Cloud Data Fusion belong to "Big Data Tools" category of the tech stack.

EventQL is an open source tool with 1.02K GitHub stars and 91 GitHub forks. Here's a link to EventQL's open source repository on GitHub.

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Detailed Comparison

EventQL
EventQL
Google Cloud Data Fusion
Google Cloud Data Fusion

EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries.

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.

Database, SQL, Analytics
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
1.2K
GitHub Stars
-
GitHub Forks
104
GitHub Forks
-
Stacks
3
Stacks
25
Followers
9
Followers
156
Votes
3
Votes
1
Pros & Cons
Pros
  • 3
    23
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
No integrations available
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to EventQL, 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.

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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