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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Dremio and Apache Flink

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.

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Replies (3)
John Nguyen
Recommends
on
AirflowAirflowAWS LambdaAWS Lambda

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

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Recommends
on
AirflowAirflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

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Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

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Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 530.9K views

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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Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 373K views
Recommends
on
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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karunakaran karthikeyan
Needs advice
on
DremioDremio
and
TalendTalend

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:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. 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.

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Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 64.9K views
Recommends
on
DremioDremio

Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.

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Pros of Dremio
Pros of Apache Flink
  • 3
    Nice GUI to enable more people to work with Data
  • 2
    Connect NoSQL databases with RDBMS
  • 2
    Easier to Deploy
  • 1
    Free
  • 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

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Cons of Dremio
Cons of Apache Flink
  • 1
    Works only on Iceberg structured data
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Dremio?

    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.

    What is 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.

    Need advice about which tool to choose?Ask the StackShare community!

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    Blog Posts

    Mar 24 2021 at 12:57PM

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    What are some alternatives to Dremio and Apache Flink?
    Presto
    Distributed SQL Query Engine for Big Data
    Apache Drill
    Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
    Denodo
    It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.
    AtScale
    Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
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