Apache Flink vs EventQL

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

EventQL

Stacks3

Followers9

Votes3

GitHub Stars1.2K

Forks104

EventQL vs Apache Flink: What are the differences?

What is EventQL? 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.

What is Apache Flink? Fast and reliable large-scale data processing engine. 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.

EventQL and Apache Flink can be categorized as "Big Data" tools.

"23" is the top reason why over 2 developers like EventQL, while over 6 developers mention "Unified batch and stream processing" as the leading cause for choosing Apache Flink.

EventQL and Apache Flink are both open source tools. Apache Flink with 9.35K GitHub stars and 5K forks on GitHub appears to be more popular than EventQL with 1.02K GitHub stars and 91 GitHub forks.

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Advice on Apache Flink, EventQL

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 Flink	EventQL
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.	EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries.
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance	Database, SQL, Analytics
Statistics
GitHub Stars 25.4K	GitHub Stars 1.2K
GitHub Forks 13.7K	GitHub Forks 104
Stacks 534	Stacks 3
Followers 879	Followers 9
Votes 38	Votes 3
Pros & Cons
Pros 16 Unified batch and stream processing 8 Out-of-the box connector to kinesis,s3,hdfs 8 Easy to use streaming apis 4 Open Source 2 Low latency	Pros 3 23
Integrations
YARN Hadoop Hadoop HBase Kafka	No integrations available

What are some alternatives to Apache Flink, EventQL?

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.

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.

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.