EventQL vs Vespa

Overview

EventQL

Stacks3

Followers9

Votes3

GitHub Stars1.2K

Forks104

Vespa

Stacks12

Followers29

Votes0

GitHub Stars6.5K

Forks675

EventQL vs Vespa: 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, Vespa is detailed as "Store, search, rank and organize big data". Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

EventQL and Vespa can be primarily classified as "Big Data" tools.

EventQL and Vespa are both open source tools. It seems that Vespa with 2.85K GitHub stars and 339 forks on GitHub has more adoption than EventQL with 1.02K GitHub stars and 91 GitHub forks.

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CLI (Node.js)

Manual

Detailed Comparison

EventQL	Vespa
EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries.	Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
Database, SQL, Analytics	-
Statistics
GitHub Stars 1.2K	GitHub Stars 6.5K
GitHub Forks 104	GitHub Forks 675
Stacks 3	Stacks 12
Followers 9	Followers 29
Votes 3	Votes 0
Pros & Cons
Pros 3 23	No community feedback yet
Integrations
No integrations available	Hadoop Pig

What are some alternatives to EventQL, Vespa?

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

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.

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

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.