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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.
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.
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.
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.
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.
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.
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.
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?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of Amazon Athena
- Use SQL to analyze CSV files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
Pros of Dremio
- Nice GUI to enable more people to work with Data3
- Connect NoSQL databases with RDBMS2
- Easier to Deploy2
- Free1
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Cons of Amazon Athena
Cons of Dremio
- Works only on Iceberg structured data1