Do you want to use Amazon Redshift Spectrum? Are you looking for a simple fix? If yes, you’ve landed at the right page! This article provides you with in-depth knowledge about AWS Redshift Spectrum, key features and some of the best practices that you can follow to boost performance and execute complex queries on your data stored in S3.
Upon a complete walkthrough of the content, you will able to use Redshift Spectrum and perform complex queries directly for your data stored in S3.
Table of Contents
- Introduction to AWS Redshift Spectrum
- Key features of AWS Redshift Spectrum
- Getting started to use Redshift Spectrum
- Exploring AWS Redshift Spectrum Best Practices
- How is AWS Redshift Spectrum different than AWS Athena?
- Choosing between Redshift Spectrum and Athena
- Amazon Redshift Spectrum: Why It Makes Sense
- Pricing model followed by AWS Redshift Spectrum
Introduction to AWS Redshift Spectrum
Amazon Redshift is a fully-managed data warehouse service provided by Amazon Web Services. It allows you to store petabytes of data into Redshift and perform complex queries. Amazon Redshift Spectrum is a service offered by Amazon Redshift that enables you to execute complex SQL queries against exabytes of structured/unstructured data stored in Amazon Simple Storage Service (S3).
You need not load the data from S3 to perform any ETL operation, AWS Redshift Spectrum will itself identify required data and load it from S3. Amazon Redshift Spectrum works on a predicate pushdown model, and it automatically creates a plan to reduce the volume of the data that needs to be read.
For further information on Redshift and Spectrum, you can check the official website here.
Key features of AWS Redshift Spectrum
Amazon Redshift Spectrum is an exceptional tool that straightforward offers to execute complex SQL queries against the data stored in Amazon S3. Check out some of its amazing features:
- Performance: Amazon Redshift Spectrum displays an exceptional performance as it queries the data, at the place where it resides. It is approximately 10x faster than other data warehouses. Redshift Spectrum also allows you to increase the data retrieval speed by setting the catching sizes as per your data needs.
- Easy to Use: Users can easily create and deploy Redshift Spectrum to manage their data needs and perform complex queries, all in a matter of minutes. You can check the official documentation of Redshift Spectrum to set it up comfortably.
- Cost-effective: Amazon Redshift Spectrum is very profitable. It is approximately ten times cheaper than a traditional data warehouse. Spectrum is quite similar to Amazon Athena, and it is charged based on the volume of the data scanned.
- Highly Scalable: Redshift Spectrum is a fully-managed platform, where all scaling operations are directly handled by Amazon, depending upon the amount of data being scanned and queried by the user.
- File Format Support: Redshift Spectrum supports complex data files such as JSON, ORC, Parquet, and also supports complex data types such as maps, arrays, structs, etc.
- Highly secure: AWS provides a security management tool known as the AWS Key Management tool. AWS allows you to isolate the data storage or warehouse by using VPC to secure the data present in your clusters and perform complex queries on it in a fault-tolerant manner.
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Getting started to use Redshift Spectrum
Redshift Spectrum increases the interoperability of your data, as you can access the same S3 object with multiple platforms like Spark, Athena, EMR, Hive, etc. allowing you to query data without performing the tedious and time-consuming extract, transfer, and load (ETL) process.
You need to set things up beforehand to get started with AWS Redshift Spectrum to perform complex querying on your data:
- Create External Tables: Amazon Redshift Spectrum uses external tables to query the data from Amazon S3. You have to create an external table on top of the data stored in S3. You can create an external table using a command similar to an SQL select statement.
- File Formats: Amazon Redshift Spectrum supports structured and semi-structured data formats that incorporate Parquet, Textfile, Sequencefile, and Rcfile. Utilizing a columnar format will improve the performance and reduce the cost as Spectrum will only pick the columns required by a query.
- Data Compression: Data compression will help you improve performance and minimize the cost incurred for scanning the data. Spectrum supports using gzip, snappy, and bz2 compression files to compress your data with ease.
- Massively Parallel Processing (MPP): To optimize the performance, Amazon suggests breaking large data files into smaller chunks of size 100 MB to 1 GB. Users can then store them for querying in Amazon S3.
To effectively use Redshift Spectrum and perform complex querying, you need to process the data beforehand, keeping in mind the points mentioned above.
Exploring AWS Redshift Spectrum Best Practices
Choosing among the prevalent standard practices to efficiently use Redshift Spectrum can be a tedious and confusing task. Incorporate the following practices to not only boost the performance of Redshift Spectrum but also to reduce your data querying costs:
- On-demand Instances: You can leverage Amazon’s on-demand functionality that automatically scales your cluster based on data volume, thereby providing concurrent workload performance and enhanced functionality.
- Columnar Storage: Storing data in a columnar format, helps you enhance the performance and reduce the query cost. Parquet and ORC are the two data storage types that store the data in a columnar way. By using columnar storage, Redshift Spectrum reads only the required columns for querying and avoids scanning the remaining columns, thereby reducing query cost.
- Data Partitioning: Partitioning the data can help you to minimize data scans and thereby reduce your incurred query cost. With Partitions, Redshift Spectrum skips the scanning of unwanted files and directly queries the required data.
- Enhancing Queries: One way to boost Spectrum’s performance is to enhance the quality of SQL queries being used to fetch data. For example, you can use the group by clause instead of the distinct function to fetch the desired data. The group by clause executes the query directly at Spectrum, rather than first transferring data to Redshift.
- Predicate Pushdown: Redshift Spectrum supports predicate pushdown, which executes the query only at the source where the data resides, thereby fetching a limited amount of data. By pushing down operations, users can boost performance by using the robust Spectrum infrastructure and also avoid consuming various resources stored in their Amazon Redshift Clusters.
How is AWS Redshift Spectrum different than AWS Athena?
AWS Redshift Spectrum works with Amazon Redshift that lets you query and process data stored in the Amazon S3 without lading it to the AWS Redshift.
AWS Athena is a serverless Analytics service to perform interactive queries over AWS S3. Since Athena is a serverless service, the user or Analyst does not have to worry about managing any infrastructure.
So what is the difference between AWS Redshift Spectrum and AWS Athena? Let’s read out some of the key differences between AWS Redshift Spectrum and Athena.
- AWS Redshift Spectrum runs with AWS Redshift or you can say specifically made to work with Redshift, while Athena is a standalone query engine that queries data in Amazon S3.
- Amazon Redshift Spectrum is usually suitable for large and complex queries directly from AWS S3, while Amazon Athena is used for running simple queries that are easy to create.
- Amazon Redshift Spectrum needs a cluster, and Amazon Athena allows for a serverless architecture.
Amazon Redshift Spectrum vs. Amazon Athena: Performance
Both the service use virtual tables to analyze data in Amazon S3 with the AWS Glue Data Catalog for managing external schemas. Users need to configure external tables in AWS Redshift Spectrum while Athena uses Glue Data Catalog metadata to directly create virtual tables. Amazon Cloud automatically allocates resources for queries for Amazon Athena, while in the case of Amazon Redshift Spectrum you have control over resource allocation.
Amazon Redshift Spectrum vs. Amazon Athena: Integrations
Amazon Redshift Spectrum allows users to run queries on data coming from external sources before loading it to Amazin S3. Amazon Athena comes with many pre-built connectors to connect to Redis, Elasticsearch, HBase, DynamoDB, DocumentDB, and CloudWatch.
Choosing between Redshift Spectrum and Athena
One should use Amazon Redshift Spectrum if the queries are tied close to Amazon Redshift because makes it easier to create table joins. And if all the data is available on Amazon S3, then one should use Amazon Athena.
Athena will do all the data reading and querying work for you until it is not a data analysis process, then go for Amazon Redshift Spectrum.
Amazon Athena relies on pooled resources which lowers the performance if you want more performance then use Redshift Spectrum but it may increase the compute usage which will increase the price. So choose the right service according to the usage.
Amazon Redshift Spectrum: Why It Makes Sense
In this section, you will read why you need to use Amazon Redshift Spectrum if you are an Amazon Redshift user then you should add it to your Data Analytics stack:
- Using Amazon Redshift Spectrum only requires defining access to external tables. All the remaining applications will remain unchanged.
- You only need to pay for the number of queries you make on data in Amazon S3. You don’t need to increase the size of the Amazon Redshift cluster.
- There is no need to load the data in the Redshift clusters as all the data remains in Amazon S3. This results in the decoupling of your storage layer in S3 from your processing layer in Redshift and Spectrum.
- Amazon Redshift does all the work of minimizing the number of AWS Redshift Spectrum nodes that are needed to access the Amazon S3 data. It also makes processing between Amazon Redshift and Amazon Redshift Spectrum efficient.
Pricing model followed by AWS Redshift Spectrum
Amazon Redshift Spectrum offers a competitive pricing model and provides users with functionalities like a pay-as-you-go pricing model, hour-based purchases, etc. Users can customize their pricing plan depending upon their data need, the number of operations, and the kind of nodes they are going to use.
For further information on Redshift’s pricing model, you can check the official documentation here.
This blog provides you with in-depth knowledge about AWS Redshift Spectrum, key features and some of the best practices that you can follow to boost performance and execute complex queries on your data stored in S3. Creating ETL Pipelines and manually pre-processing data to make it analysis-ready can be challenging, especially for a beginner & this is where Hevo saves the day.
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Why don’t you share your experience of using AWS Redshift Spectrum in the comments? We would love to hear from you!