Amazon Athena vs Dremio

Overview

Amazon Athena

Stacks519

Followers840

Votes49

Dremio

Stacks116

Followers348

Votes8

Amazon Athena vs Dremio: What are the differences?

Cost: One key difference between Amazon Athena and Dremio is the cost. In the case of Amazon Athena, users are billed based on the amount of data scanned during query execution. On the other hand, Dremio offers a subscription-based pricing model, which can be a more cost-effective option for some organizations.
Performance: When it comes to performance, Dremio has an advantage over Amazon Athena. Dremio utilizes a massively parallel processing architecture, which allows it to deliver faster query speeds by distributing workloads across multiple nodes. This can be especially beneficial when dealing with large data sets.
Data Sources: Another difference between the two is the range of data sources they support. Amazon Athena is specifically designed for querying data stored in Amazon S3. On the contrary, Dremio supports a wider variety of data sources, including cloud-based storage systems like Amazon S3, Hadoop Distributed File System (HDFS), and relational databases like MySQL and PostgreSQL.
Data Transformation: Dremio provides more extensive data transformation capabilities compared to Amazon Athena. With Dremio, users can perform complex data transformations on-the-fly while querying, allowing for data cleansing, enrichment, and preparation without the need for additional ETL processes. Amazon Athena, on the other hand, has limited data manipulation capabilities and is more focused on querying and analysis.
Data Catalog: Dremio offers a centralized data catalog feature, which allows users to easily discover and access data sources from a single location. It provides a unified view of all available data sources and enables users to search, browse, and manage metadata. Amazon Athena, on the other hand, lacks a built-in data catalog and relies on external services like AWS Glue for cataloging and metadata management.
Security and Access Control: When it comes to security and access control, both Amazon Athena and Dremio offer similar features such as encryption at rest and in transit, as well as support for AWS Identity and Access Management (IAM) for user authentication and authorization. However, Dremio provides more granular access control options, allowing users to define fine-grained access policies based on different attributes, such as user roles or data sources.

In Summary, Amazon Athena is a cost-effective option for querying data stored in Amazon S3, while Dremio offers faster performance, supports a wider range of data sources, provides extensive data transformation capabilities, and offers a centralized data catalog and granular access control options.

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Advice on Amazon Athena, 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

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

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

522k views522k

Comments

Detailed Comparison

Amazon Athena	Dremio
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.	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 519	Stacks 116
Followers 840	Followers 348
Votes 49	Votes 8
Pros & Cons
Pros 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay	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
Amazon S3 Presto	Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI

What are some alternatives to Amazon Athena, 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.

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.

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.

Related Comparisons

Amazon Athena vs Dremio: What are the differences?

Cost: One key difference between Amazon Athena and Dremio is the cost. In the case of Amazon Athena, users are billed based on the amount of data scanned during query execution. On the other hand, Dremio offers a subscription-based pricing model, which can be a more cost-effective option for some organizations.
Performance: When it comes to performance, Dremio has an advantage over Amazon Athena. Dremio utilizes a massively parallel processing architecture, which allows it to deliver faster query speeds by distributing workloads across multiple nodes. This can be especially beneficial when dealing with large data sets.
Data Sources: Another difference between the two is the range of data sources they support. Amazon Athena is specifically designed for querying data stored in Amazon S3. On the contrary, Dremio supports a wider variety of data sources, including cloud-based storage systems like Amazon S3, Hadoop Distributed File System (HDFS), and relational databases like MySQL and PostgreSQL.
Data Transformation: Dremio provides more extensive data transformation capabilities compared to Amazon Athena. With Dremio, users can perform complex data transformations on-the-fly while querying, allowing for data cleansing, enrichment, and preparation without the need for additional ETL processes. Amazon Athena, on the other hand, has limited data manipulation capabilities and is more focused on querying and analysis.
Data Catalog: Dremio offers a centralized data catalog feature, which allows users to easily discover and access data sources from a single location. It provides a unified view of all available data sources and enables users to search, browse, and manage metadata. Amazon Athena, on the other hand, lacks a built-in data catalog and relies on external services like AWS Glue for cataloging and metadata management.
Security and Access Control: When it comes to security and access control, both Amazon Athena and Dremio offer similar features such as encryption at rest and in transit, as well as support for AWS Identity and Access Management (IAM) for user authentication and authorization. However, Dremio provides more granular access control options, allowing users to define fine-grained access policies based on different attributes, such as user roles or data sources.

Amazon Athena vs Dremio

Overview