It makes it possible, for instance, to join data in external tables with data stored in Amazon Redshift to run complex queries. In other words, it needs to know ahead of time how the data is structured, is it a Parquet file? Both the services use Glue Data Catalog for managing external schemas. You can extend Athena via federated query services. For more information on Xplenty's native Redshift connector, visit our Integration page. But in order to do that, Redshift needs to parse the raw data files into a tabular format. Before you choose between the two query engines, check if they are compatible with your preferred analytic tools. Redshift Spectrum is great for Redshift customers. Some popular ones include: At a quick glance, Redshift Spectrum and Athena, both, seem to offer the same functionality - serverless query of data in Amazon S3 using SQL. Explore, If you have a story to tell, knowledge to share, or a perspective to offer — welcome home. Why? This question about interactive query services AWS Athena and Redshift Spectrum has come up a few times in various posts and forums. Additionally, several Redshift clusters can access the same data lake simultaneously. Subscribe. Amazon Athena is a serverless query processing engine based on open source Presto. I would approach this question, not from a technical perspective, but what may already be in place (or not in place). Assuming you have objects on S3 that Athena can consume, then you might start with Athena vs. spinning up Redshift clusters. It is important to note that you need Redshift to run Redshift Spectrum. Data Storage Formats Supported by Redshift and Athena Redshift data warehouse only supports structured data at the node level. You don't need to maintain any clusters with Athena. When using Spectrum, you have control over resource allocation, since the size of resources depends on your Redshift cluster. … In both cases, you pay for each terabyte of data scanned. Rather than try to decipher technical differences, the post frames the choice as a buying, or value, question. Athena and Spectrum are both charged based on the data scanned when running a query. Redshift takes much longer to set up. Whether you are a team of one or a group of 100, the last thing you need is to fly blind and get stuck with self-service (aka, no service) solutions. With Redshift Spectrum, on the other hand, you need to configure external tables for each external schema. Having Parquet on s3 can be an effective strategy for teams that want to partition data where residents within Redshift and other data are resident on S3. Athena can connect to Redis, Elasticsearch, HBase, DynamoDB, DocumentDB, and CloudWatch. A query in Athena and Spectrum generally has the same cost basis of $5 per terabyte scanned. The total cost is calculated according to the amount of data you scan per query. Looker also released support for Athena. Fundamental Features. Our approach unlocks how quickly users can undertake data ingestion to a data lake so they can return query results rapidly. Redshift Spectrum runs in tandem with Amazon Redshift, while Athena is a standalone query engine for querying data stored in Amazon S3, With Redshift Spectrum, you have control over resource provisioning, while in the case of Athena, AWS allocates resources automatically, Performance of Redshift Spectrum depends on your Redshift cluster resources and optimization of S3 storage, while the performance of Athena only depends on S3 optimization, Redshift Spectrum can be more consistent performance-wise while querying in Athena can be slow during peak hours since it runs on pooled resources, Redshift Spectrum is more suitable for running large, complex queries, while Athena is more suited for simplifying interactive queries, Redshift Spectrum needs cluster management, while Athena allows for a truly serverless architecture. With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” Athena is focused on extract, transform and load (ETL) data from S3 and has a good integration with AWS Glue: Athena is easy to use. Spectrum can directly join tables stored on Redshift. Check your inboxMedium sent you an email at to complete your subscription. Spectrum is a serverless query processing engine that allows to join data that sits in Amazon S3 with data in Amazon Redshift. If you are not an Amazon Redshift customer, running Redshift Spectrum together with Redshift can be very costly. To decide between the two, consider the following factors: For existing Redshift customers, Spectrum might be a better choice than Athena. Xplenty helps 1000s of customers cut weeks of development time with out-of-the box integrations that connect 100s of popular data sources and SaaS applications. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. IN 28 MINUTES COURSE VIDEOS FREE COURSE. If you are asking yourself, “What is a Data Lake?” we cover the basics, which may help you pick the right path. One significant difference is that Spectrum requires Redshift, which must be factored into your total cost. Athena vs. Redshift Spectrum vs. Presto. With regard to all basic table scans and small aggregations, Amazon Athena stands out as more effective in comparison with Amazon Redshift. Dave Schuman The cluster and the data files in Amazon S3 must be in the same AWS Region. J'évalue en quelque sorte Athena et Redshift Spectrum. If you are not a Redshift customer, Athena might be a better choice. AWS Athena is based on Facebook Presto and includes some Apache Hive goodness. If Redshift is required on day 1, it might be a good idea to use Redshift with Redshift Spectrum (query external tables from S3 with the same pricing model as Athena) to combine the best of both … It’s intuitive, it’s easy to deal with [...] and when it gets a little too confusing for us, [Xplenty’s customer support team] will work for an entire day sometimes on just trying to help us solve our problem, and, For more information on Xplenty's native Redshift connector, visit our, Functionality and Performance Comparison for Redshift Spectrum vs. Athena, Redshift Spectrum vs. Athena Integrations, Redshift Spectrum vs. Athena Cost Comparison. Amazon RedShift vs RedShift Spectrum vs Amazon EMR - A comparison - AWS Certification Cheat Sheet Jul 15, 2020 3 minute read Let’s get a quick overview of the big data options in AWS - Amazon RedShift vs RedShift Spectrum vs Amazon … Amazon Athena, on the other hand, is a standalone query engine that uses SQL to directly query data stored in Amazon S3. Have data in locations other than your data lake? data warehouse. Note: You are still paying “per query” for the amount of data scanned via Spectrum the same as Athena. They really have provided an interface to this world of data transformation that works. Both the services use OBDC and JBDC drivers for connecting to external tools. The service allows data analysts to run queries on data stored in S3. This does not have to be an AWS Athena vs. Redshift choice. Redshift Spectrum vs. Athena Cost Spectrum and Athena are both charged based on the amount of data scanned when running a query – although there is 10MB minimum per query and AWS rounds up to the next megabyte. You can build a truly serverless architecture. The actual real world performance of Athena vs. Redshift Spectrum is difficult to measure since with Athena you don't know how much capacity you get (but it's a lot) and in Redshift Spectrum you get a dedicated capacity that is dependent on your cluster size. Redshift Spectrum vs. Athena Amazon Athena is similar to Redshift Spectrum, though the two services typically address different needs. We will look at important certification questions regarding Amazon RedShift vs RedShift Spectrum vs Amazon EMR. It has never been easier to take advantage of an analytics-ready data lake with Amazon Athena and Redshift Spectrum interactive query services. It’s easy and free to post your thinking on any topic. Did you know that Power BI supports Athena via ODBC connections? Initialization Time. This approach can minimize the need to scale Redshift requires a new node for improving performance, which can be expensive! Redshift Spectrum runs in tandem with Amazon Redshift, while Athena is a standalone query engine for querying data stored in Amazon S3 With Redshift Spectrum, you have control over resource provisioning, while in the case of Athena, AWS allocates resources automatically There is no need to manage any infrastructure. For this example, the sample data is in the US West (Oregon) Region (us-west-2), so you need a cluster that is also in us-west-2. However, if you are using both together, you should look closely at your architecture if this occurs. FIND OUT IF WE CAN INTEGRATE YOUR DATA Write on Medium, Building A Serverless Business Intelligence Stack With Apache Parquet, Tableau, and Amazon Athena, Power BI supports Athena via ODBC connections, Amazon Finance API for FBA Acquisition And Seller Growth, AWS Data Lake And Amazon Athena Federated Queries, How To Automate Adobe Data Warehouse Exports, Sailthru Connect: Code-free, Automation To Data Lakes or Cloud Warehouses, Unlocking Amazon Vendor Central Data With New API, Amazon Seller Analytics: Products, Competitors & Fees, Amazon Remote Fulfillment FBA Simplifies ExpansionTo New Markets. However, Redshift Spectrum tables do also support other storage formats ie. BTW Athena comes with a … This article explores how to use Xplenty with two of them (Time Travel and Zero Copy Cloning). Both services follow the same pricing structure. It might be the case that your analytic tool of choice does not support Athena, but does support Redshift. Thus, if you want extra-fast results for a query, you can allocate more computational resources to it when running Redshift Spectrum. It’s intuitive, it’s easy to deal with [...] and when it gets a little too confusing for us, [Xplenty’s customer support team] will work for an entire day sometimes on just trying to help us solve our problem, and they never give up until it’s solved. The cost of running Redshift, on average, is approximately $1,000 per TB, per year. Doing so reduces the size of your Redshift cluster, and consequently, your annual bill. ETL is a much more secure process compared to ELT, especially when there is sensitive information involved. CTO and Co-Founder at Raise.me The same would apply for AWS Redshift Spectrum, especially given that AWS Redshift is a Data Warehousing technology. Redshift federated queries were released in 2020. These new capabilities may tip the scales in favor of sticking with Redshift. How to tune your Amazon Athena … This may be attractive given the fact many teams may not want to run, maintain, or pay for a set of Amazon Redshift clusters. It’s a common misconception that Spectrum uses Athena under the hood to query the S3 data files. Existe-t-il des inconvénients spécifiques pour le spectre Athena ou Redshift? Building data platforms and data infrastructure is hard work. Athena can be an exceptional value when implemented correctly, especially when paired with analytics services that support data caches like Tableau. As Spectrum is still a developing tool and they are kind of adding some features like transactions to make it more efficient. ELT and ETL data ingestion pipelines to data lakes or warehouses. If you are not careful, you could have increased the costs of maintaining this kind of stack. Review our Privacy Policy for more information about our privacy practices. However, Athena is good for initial exploratory analysis to be done on any data stored in S3. Lastly, regardless of a Spectrum or Athena choice, do not overlook data formats optimizations external tables need to drive efficiency and downward costs. They really have provided an interface to this world of data transformation that works. You do not have control over resource provisioning. I can query a 1 TB Parquet file on S3 in Athena the same as Spectrum. They can leverage Spectrum to increase their data warehouse capacity without scaling up Redshift. For example, let’s assume you have about 4 TB of data in a historical_purchase table in Redshift. The benefit of this approach is offloading data so you can be more efficient with local storage in Redshift. But it’s not true. Setting up Amazon Athena. How companies should avoid creating a slow many headed federated Gorgon out of out of Athena. Amazon Athena vs Redshift: Base Comparison. Check out the post Building A Serverless Business Intelligence Stack With Apache Parquet, Tableau, and Amazon Athena for inspiration. This would be good for data warehouses which don’t need to be queried often but are large in size. One significant difference is that Spectrum requires Redshift, which must be factored into your total cost. Using Athena opens up an opportunity to create a full serverless data analytics stack. Intrigued? Access to Spectrum requires an active, running Redshift instance. If you went down the Athena path, your tool choices might be more limited than Redshift. In doing so, we will consider some of the fundamental characteristics concerning both the services. For example, you can store infrequently used data in Amazon S3 and frequently stored data in Redshift. The cost of running queries in Redshift Spectrum and Athena is $5 per TB of scanned data. More importantly, consider the cost of running Amazon Redshift together with Redshift Spectrum. Xplenty lets you build ETL data pipelines in no time. Please read our blog Face off: AWS Athena vs Redshift Spectrum – which service you should use and when. Why we chose Redshift…at first. Access to the “Redshift+Redshift Spectrum” tandem has costs that might not be worthwhile (right now) if you are NOT an AWS Redshift customer. But let’s go back to the beginning. Using the visual interface, you can quickly start integrating Amazon Redshift, Amazon S3, and other popular databases. Data optimized on S3 in the Apache Parquet format is well-positioned for Athena AND Spectrum. For example, let’s say you have a 100 GB transactional table of infrequently accessed data. S3 Select vs Athena vs Redshift Spectrum S3 - Analytics - Select, Athena, Spectrum. Amazon S3 allows you to run sophisticated Big Data analytics on your data without moving the data into a separate analytics system. More importantly, with Federated Query, you can perform complex transformations on data stored in external sources before loading it into Redshift. The price is the same across both services – $5 per compressed terabyte scanned. We suggest that you test a tool that works with Athena, Redshift, and Redshift Spectrum. If your team of analysts is frequently using S3 data to run queries, calculate the cost vis-a-vis storing your entire data in Redshift clusters. Athena utilise Presto et Spectrum utilise le moteur de son Redshift. Another great side effect of having a schema catalog in Glue, you can use the data with more than just Redshift Spectrum. Athena requires zero infrastructure—it directly queries data already stored on Amazon S3. Additional costs to take into account could be storage on S3, which is relatively less costly than a database. Amazon Redshift Spectrum vs. Athena: Which One to Choose? Lyft , Coursera , and 9GAG are some of the popular companies that use Amazon Redshift, whereas Amazon Redshift Spectrum is used by VSCO , CommonBond , and intermix.io . Athena has prebuilt connectors that let you load data from sources other than Amazon S3. TRUSTED BY COMPANIES WORLDWIDE There is no need to spin up a Redshift cluster for using Athena. Presto is for everything else, including large data sets, more regular analytics, and higher user concurrency. If Athena is a fit for your workload, saving time and money is well within reach. Learn some simple rules of thumb you can use to choose the best federated query engine for your company's needs. Amazon Redshift can be classified as a tool in the "Big Data as a Service" category, while Amazon Redshift Spectrum is grouped under "Big Data Tools". They use virtual tables to analyze data in Amazon S3. The price is the same for both services – $5 per compressed terabyte scanned. Nothing stops you from using both Athena or Spectrum. This could be a deal-breaker for some. What is Amazon Redshift Spectrum? However, the two differ in their functionality. While the two looks similar, Redshift actually loads and queries that data on it’s own, directly from S3. Much like Redshift Spectrum, Athena is serverless. Get a detailed comparison of their performances and speeds before you commit. After getting the basic overview of both the services, lets run a comparison between the two to find out which one is a better choice. You can run your queries directly in Athena. Our AWS lake formation service optimizes and automates the configuration, processing, organizing, and loading of data for use in Athena and Spectrum. You don't need to maintain any infrastructure, which makes them incredibly cost-effective. Schedule a call and learn how our low-code platform makes data integration seem like child's play. This means that you can get up and running at low or no cost. Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table. For example, Tableau 10.3 officially released support for Athena. Amazon Athena is easy to set up; it is a serverless service which can be accessed directly from the AWS Management Console with a few clicks. You only pay for the queries you run. You can query the data using Athena (Presto), write Glue ETL jobs, access the formatted data from EMR and Spark, and join your data with many other SQL databases in the AWS ecosystem. Thus, performance can be slow during peak hours. Enjoying This Article? Athena does not Redshift Spectrum is an extension of Amazon Redshift. Redshift is tailored for frequently accessed data that needs to be stored in a consistent, highly structured format. Amazon Athena and Amazon Redshift Spectrum enable you to run Amazon SQL queries against data in Amazon S3. parquet, orc, etc. Athena is a Serverless querying service provided by AWS which can also be used to query data stored in S3. A query in Athena and Spectrum generally has the same cost basis of $5 per terabyte scanned. Not a big deal, but make sure any ETL or ELT data processing for use within Spectrum should account for external tables. However, there may be tools that don’t support Athena that you rely on. Why pay to store that data in Redshift when moving it to external tables on AWS S3 and query data with Spectrum is an option? Athena and Redshift Spectrum provide compelling, cost-effective solutions to query the contents of your lake. Code-free, fully-automated ELT/ETL data ingestion fuels Azure, Athena, Redshift Spectrum data lakes or AWS Redshift and Google BigQuery cloud warehouses, Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Choose the solution that’s right for your business, Streamline your marketing efforts and ensure that they're always effective and up-to-date, Generate more revenue and improve your long-term business strategies, Gain key customer insights, lower your churn, and improve your long-term strategies, Optimize your development, free up your engineering resources and get faster uptimes, Maximize customer satisfaction and brand loyalty, Increase security and optimize long-term strategies, Gain cross-channel visibility and centralize your marketing reporting, See how users in all industries are using Xplenty to improve their businesses, Gain key insights, practical advice, how-to guidance and more, Dive deeper with rich insights and practical information, Learn how to configure and use the Xplenty platform, Use Xplenty to manipulate your data without using up your engineering resources, Keep up on the latest with the Xplenty blog. Partitioning. To use Redshift Spectrum, you need an Amazon Redshift cluster and a SQL client that's connected to your cluster so that you can execute SQL commands. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Athena is able to work with S3 buckets from different regions, while Redshift Spectrum is able to load data only from buckets within the region. Results of queries run on Athena can be stored on S3 and loaded to Redshift if needed. Choosing Between The Best Federated Query Engine And a Data Warehouse. On the other hand, Athena supports a large number of storage formats ie. Tags: However, in the case of Athena, it uses Glue Data Catalog's metadata directly to create virtual tables. You simply point Athena to your data stored on Amazon S3 and you’re good to go. As an existing Redshift user, I would be less inclined to use Athena because of existing investments in Redshift. Assuming you have objects on S3 that Athena can consume, then you might start with Athena vs. spinning up Redshift clusters. Keith connected multiple data sources with Amazon Redshift to transform, organize and analyze their customer data. Lastly, remember that access to Spectrum requires an active, running Redshift instance. Customer Story By signing up, you will create a Medium account if you don’t already have one. Athena is a great choice for getting started with analytics if you have nothing set up yet. Check your tools, can then access Athena via ODBC or JDBC? Let's take a closer look at the differences between Amazon Redshift Spectrum and Amazon Athena. Want to read some more? A key difference between Redshift Spectrum and Athena is resource provisioning. The underlying recommendation for deciding between Athena and Redshift is to start with Athena and move some of the query-intensive use cases to Redshift when reaching the cost tipping point’. Snowflake, the Elastic Data Warehouse in the Cloud, has several exciting features. In the case of Athena, the Amazon Cloud automatically allocates resources for your query. You can get Athena up and running in minutes. This is also true in moving for Apache Parquet data from S3 Data Lake to a Microsoft Azure Data Lake! Lastly, Athena is an on-demand, serverless query engine. Robert Meyer. Amazon Athena is a serverless query service, so there is no infrastructure to set up or manage. Amazon Redshift Keith connected multiple data sources with Amazon Redshift to transform, organize and analyze their customer data. An analyst that already works with Redshift will benefit most from Redshift Spectrum because it can quickly access data in the cluster and extend out to infrequently accessed, external tables in S3. However, you can only analyze data in the same AWS region.