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  5. Dremio vs Talend

Dremio vs Talend

OverviewDecisionsComparisonAlternatives

Overview

Talend
Talend
Stacks297
Followers249
Votes0
Dremio
Dremio
Stacks116
Followers348
Votes8

Dremio vs Talend: What are the differences?

<Write Introduction here>
  1. Architecture: Dremio is built as a SQL analytics engine with a focus on accelerating queries directly on data lake storage, while Talend is a data integration platform that offers ETL and data quality capabilities.
  2. User Interface: Dremio provides a user-friendly and interactive UI for data analysts and business users to query and visualize data, whereas Talend offers a more developer-centric UI for designing and deploying data integration workflows.
  3. Cost Model: Dremio follows a subscription-based pricing model that includes enterprise features and support, while Talend offers various pricing tiers based on the number of users and data sources.
  4. Data Processing: Dremio utilizes a distributed SQL query engine to process data at scale, enabling high-performance analytics on large datasets, whereas Talend leverages its ETL capabilities for data processing and transformation tasks.
  5. Connectivity: Dremio natively integrates with popular data sources like S3, HDFS, and NoSQL databases for seamless data access, while Talend provides a wide range of connectors and components to connect to different systems and applications.
  6. Scalability: Dremio is designed to scale horizontally by adding more nodes to the cluster, offering increased processing power and storage capacity, whereas Talend's scalability is more dependent on the underlying infrastructure and resources available.
In Summary, Dremio and Talend differ in architecture, user interface, cost model, data processing, connectivity, and scalability capabilities.

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Advice on Talend, Dremio

karunakaran
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:

  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.

80.5k views80.5k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

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

Talend
Talend
Dremio
Dremio

It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.

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.

-
Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
Stacks
297
Stacks
116
Followers
249
Followers
348
Votes
0
Votes
8
Pros & Cons
No community feedback yet
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
No integrations available
Amazon S3
Amazon S3
Python
Python
Tableau
Tableau
Azure Database for PostgreSQL
Azure Database for PostgreSQL
Qlik Sense
Qlik Sense
PowerBI
PowerBI

What are some alternatives to Talend, Dremio?

Google BigQuery

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

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

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

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

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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.

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