Apache Flink vs Dremio

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Dremio

Stacks116

Followers348

Votes8

Apache Flink vs Dremio: What are the differences?

Introduction

Apache Flink and Dremio are both powerful tools in the data processing and analytics space. Below are the key differences between Apache Flink and Dremio.

Architecture: Apache Flink is a stream processing framework that enables high-throughput, low-latency data analytics while Dremio focuses on data virtualization and querying data across various sources in real-time. Flink is designed for real-time streaming and batch processing, while Dremio is more focused on providing a unified view of data from multiple sources.
Use Cases: Apache Flink is commonly used for real-time data processing, streaming analytics, and complex event processing. On the other hand, Dremio is ideal for self-service data exploration, data virtualization, accelerating queries on data lakes, cloud data warehouses, and other data sources.
Programming Language Support: Apache Flink primarily supports Java and Scala for writing data processing applications, while Dremio supports SQL queries to interact with various data sources using its SQL engine. Dremio also provides REST APIs for programmatic interaction.
Data Storage and Management: Apache Flink does not focus on data storage and management but rather data processing. In contrast, Dremio provides a data reflection engine that optimizes and accelerates queries by creating reflection caches and materialized views for data stored in various sources.
Community and Ecosystem: Apache Flink has a large and active open-source community with a wide range of connectors and integrations with other tools such as Apache Kafka, Apache Hadoop, and more. Dremio also has a growing community but focuses more on its proprietary data virtualization platform.
Deployment: Apache Flink can be deployed in standalone mode, on YARN, Mesos, Kubernetes, or can also run on cloud platforms like AWS and Azure. Dremio typically runs as a virtualization engine on-premises or on cloud environments like AWS, Azure, or Google Cloud Platform.

Summary

In Summary, Apache Flink is a stream processing framework focused on real-time analytics, while Dremio is a data virtualization platform that provides a unified view of data from various sources.

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Advice on Apache Flink, Dremio

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k

Comments

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.

576k views576k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Detailed Comparison

Apache Flink	Dremio
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.	Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.
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	Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 116
Followers 879	Followers 348
Votes 38	Votes 8
Pros & Cons
Pros 16 Unified batch and stream processing 8 Easy to use streaming apis 8 Out-of-the box connector to kinesis,s3,hdfs 4 Open Source 2 Low latency	Pros 3 Nice GUI to enable more people to work with Data 2 Easier to Deploy 2 Connect NoSQL databases with RDBMS 1 Free Cons 1 Works only on Iceberg structured data
Integrations
YARN Hadoop Hadoop HBase Kafka	Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI

What are some alternatives to Apache Flink, Dremio?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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Apache Flink vs Dremio: What are the differences?

Introduction

Apache Flink and Dremio are both powerful tools in the data processing and analytics space. Below are the key differences between Apache Flink and Dremio.

Architecture: Apache Flink is a stream processing framework that enables high-throughput, low-latency data analytics while Dremio focuses on data virtualization and querying data across various sources in real-time. Flink is designed for real-time streaming and batch processing, while Dremio is more focused on providing a unified view of data from multiple sources.
Use Cases: Apache Flink is commonly used for real-time data processing, streaming analytics, and complex event processing. On the other hand, Dremio is ideal for self-service data exploration, data virtualization, accelerating queries on data lakes, cloud data warehouses, and other data sources.
Programming Language Support: Apache Flink primarily supports Java and Scala for writing data processing applications, while Dremio supports SQL queries to interact with various data sources using its SQL engine. Dremio also provides REST APIs for programmatic interaction.
Data Storage and Management: Apache Flink does not focus on data storage and management but rather data processing. In contrast, Dremio provides a data reflection engine that optimizes and accelerates queries by creating reflection caches and materialized views for data stored in various sources.
Community and Ecosystem: Apache Flink has a large and active open-source community with a wide range of connectors and integrations with other tools such as Apache Kafka, Apache Hadoop, and more. Dremio also has a growing community but focuses more on its proprietary data virtualization platform.
Deployment: Apache Flink can be deployed in standalone mode, on YARN, Mesos, Kubernetes, or can also run on cloud platforms like AWS and Azure. Dremio typically runs as a virtualization engine on-premises or on cloud environments like AWS, Azure, or Google Cloud Platform.

Summary

In Summary, Apache Flink is a stream processing framework focused on real-time analytics, while Dremio is a data virtualization platform that provides a unified view of data from various sources.