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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.https://dudodiprj2sv7.cloudfront.net/product-logos/ri/0H/K93ECPX8DWIX.pngAmazon RedShift happy userAmazon RedShift is used primarily by our in house data analytics and psychometric department. When we process the results of physician board certification exams we have a workflow which integrates the newly processed scoring data into RedShift which is used for analytical purposes. The data is denormalized and stored in a fashion which makes it overall more optimal for querying purposes. This data serves as the basis for final scoring calculations for a given exam. The data analytics department is then able to run their statistical analysis against the housed sets of data to ultimately determine the final test scores. We also house an "item bank" of exam questions from past exams which are used as the basis for future exams. Scoring is correlated to the item bank to help determine which questions performed well and which ones exam takers had difficulty with. RedShift is a great tool for our IT department because it helps share the responsibility of having to secure such sensitive data. Amazon is a company which we feel very confident with and RedShift has proven to be extremely robust and fast. It is nice to be able to so easily perform backups of data and rest more assured that it is in a safe form and not something which our own IT department has to manage along with all of our internally hosted transactional databases.,Extremely fast querying allowing for concurrent analysis. PostgreSQL syntax which allows for developers with a SQL background to easily begin working with the data. Multiple output formats including JSON. Safe, easy, and reliable backups.,SQL syntax support is not 100% which can lead to frustrating situations when developing a query. No support for database keys. No stored procedure support.,9,It allows for an almost seamless integration of our data which can then be used by other departments for analytical purposes. No in house resources are needed for keeping the data alive and performing backup/migration tasks of the data in its end state.,Microsoft SQL Server, SQL Server Integration Services, MySQL,10A hands-free data warehouseWe use Amazon Redshift and Redshift Spectrum for our data warehouse. Our production transactional datastores are continuously replicated to Redshift and transformed into fact tables. Redshift is maintained by the data team, but it is used by analysts on most teams, including business intelligence, product, and customer support. Redshift is our source of truth; it provides information about business processes that the team needs to make decisions.,Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms. Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster. Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box. Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.,We've experienced some problems with hanging queries on Redshift Spectrum/external tables. We've had to roll back to and old version of Redshift while we wait for AWS to provide a patch. Redshift's dialect is most similar to that of PostgreSQL 8. It lacks many modern features and data types. Constraints are not enforced. We must rely on other means to verify the integrity of transformed tables.,8,It is essential for all teams to refer to the same source of truth. Redshift serves as that store of truth. Product managers and analysts can use a variety of clients to answer their own questions; data analysts are not overwhelmed with ad-hoc queries. Redshift is fully managed; our engineers spend their time building features rather than maintaining infrastructure. It is often simpler to write objects to S3 than load data into a table; Spectrum provides a useful shortcut for tedious engineering work.,PostgreSQL,PyCharm, IntelliJ IDEA, AWS LambdaGreat for "Big Data" -- but not so much otherwiseRedshift is currently being used to house normalized client data pulled from various third-party endpoints. It houses the data that is both being accessed directly by our business intelligence and CRM platform, as well as made available via our own API gateways. It was chosen for its ability to support a "big data" environment with high availability.,If you need draw insights from immense amounts (see: petabytes) of transactional (repetitive) data in near real time--think machine learning and business intelligence--and you're already in the AWS ecosystem, then it's your only real option. It performs very well. Highly configurable, intelligent compression of repetitive columns reduces your memory footprint, lending to extremely high performance. As with most things in the AWS ecosystem, it scales seamlessly and endlessly.,There is no support for data de-duplication; meaning this has to be either accounted for upstream, or you'll have to build your own services to de-dupe your data. It's strength is housing data, not necessarily data insertions. While it has an SQL-like interface, it shouldn't be approached the same as a typical relational database. Permissions can be a pain... dovetailing on my previous "con" , in some instances it's easier to drop/rebuild a table than try to navigate incremental updates/insertions, but retaining user-permissions is a pain-point.,7,To be honest, we haven't yet seen the return, as we aren't yet operating at scale. So we haven't seen the positive impacts, only the added engineering overhead of dealing with Redshift's eccentricities.Amazon Redshift - a new generation of data warehouse platformAmazon Redshift is being used as the main data warehouse across the whole organization. It provides essential business performance data and valuable intelligence. Different departments utilize it for a variety of operations, and decision makers rely on the data analytic tools built upon its data for tracking and enhancement, wherever needed.,It does very well in data ingestion, and compresses data efficiently. Most of the queries return results quickly even with large data sets.,It has a hard limit of total number of concurrent connections to the database. Compared with conventional databases that limit is very low. Its workload management (WLM) mechanism could be improved, such as made more dynamic and easier to tune and manage.,8,Data loading is much faster Database physical footprint got much smaller No need for our own data center and hardware,Oracle Database Cloud ServiceAmazon Redshift: a fast, scalable and easy to use data warehouse serviceWe use Amazon Redshift as our data warehouse. We store our most used datasets on Redshift. Some of the data transformations are also made on Redshift but we try to avoid to do the heavy transformation there. Our dashboarding tool sits on top of Redshift and runs queries on every dashboard load. Analysts are using Redshift all day as the main source of data. Data savvy engineers and product managers have read access.,It is easy to use. SQL is one of the easiest languages to learn. Fast. Especially if you use an SSD cluster. Scalable. If you need more space for your data or want faster results you can add more nodes.,It is PostgreSQL. I miss some commands and procedural features. It is perfect for analytics purposes, but not fast enough for web apps. Too expensive for ETL processing.,9,Amazon Redshift is our main data source. We use it for almost every analysis we made. These analyses are driving our decisions and strategy. Prezi is a data-driven company. Every decision we make is based on an analyses or a dashboard using datasets on Redshift.,Microsoft SQL Server and Oracle Data Warehouse,Apache Spark, Apache Hive, Chartio
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Amazon Redshift
92 Ratings
Score 8.5 out of 101
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Amazon Redshift Reviews

Amazon Redshift
92 Ratings
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Score 8.5 out of 101
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Brendan McKenna profile photo
February 01, 2019

User Review: "Amazon RedShift happy user"

Score 9 out of 10
Vetted Review
Verified User
Review Source
Amazon RedShift is used primarily by our in house data analytics and psychometric department. When we process the results of physician board certification exams we have a workflow which integrates the newly processed scoring data into RedShift which is used for analytical purposes. The data is denormalized and stored in a fashion which makes it overall more optimal for querying purposes. This data serves as the basis for final scoring calculations for a given exam. The data analytics department is then able to run their statistical analysis against the housed sets of data to ultimately determine the final test scores. We also house an "item bank" of exam questions from past exams which are used as the basis for future exams. Scoring is correlated to the item bank to help determine which questions performed well and which ones exam takers had difficulty with. RedShift is a great tool for our IT department because it helps share the responsibility of having to secure such sensitive data. Amazon is a company which we feel very confident with and RedShift has proven to be extremely robust and fast. It is nice to be able to so easily perform backups of data and rest more assured that it is in a safe form and not something which our own IT department has to manage along with all of our internally hosted transactional databases.
  • Extremely fast querying allowing for concurrent analysis.
  • PostgreSQL syntax which allows for developers with a SQL background to easily begin working with the data.
  • Multiple output formats including JSON.
  • Safe, easy, and reliable backups.
  • SQL syntax support is not 100% which can lead to frustrating situations when developing a query.
  • No support for database keys.
  • No stored procedure support.
Very well suited for aggregating/denormalizing data when you need a reporting environment. Can provide extremely fast querying for analytical purposes. Very nice to not have to have in house responsibility for sensitive data.

Not appropriate for a transactional system (though this is not what it is built for obviously). Must keep in mind the data you are syncing up to the cloud and scrub if necessary before. Something to always be mindful of of course.
Read Brendan McKenna's full review
Gavin Hackeling profile photo
October 12, 2018

Amazon Redshift Review: "A hands-free data warehouse"

Score 8 out of 10
Vetted Review
Verified User
Review Source
We use Amazon Redshift and Redshift Spectrum for our data warehouse. Our production transactional datastores are continuously replicated to Redshift and transformed into fact tables. Redshift is maintained by the data team, but it is used by analysts on most teams, including business intelligence, product, and customer support. Redshift is our source of truth; it provides information about business processes that the team needs to make decisions.
  • Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms.
  • Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster.
  • Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box.
  • Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.
  • We've experienced some problems with hanging queries on Redshift Spectrum/external tables. We've had to roll back to and old version of Redshift while we wait for AWS to provide a patch.
  • Redshift's dialect is most similar to that of PostgreSQL 8. It lacks many modern features and data types.
  • Constraints are not enforced. We must rely on other means to verify the integrity of transformed tables.
Redshift is ideal for small teams. It is fully managed. CloudWatch metrics are provided out-of-the-box, and it integrates well with other AWS products, such as DMS. The Redshift console is among the better AWS consoles. Redshift offers adequate performance. Spectrum offers a convenient way to access our data lake, but we have encountered issues with recent versions.
Read Gavin Hackeling's full review
Kyle Reichelt profile photo
November 25, 2018

Amazon Redshift Review: "Great for "Big Data" -- but not so much otherwise"

Score 7 out of 10
Vetted Review
Verified User
Review Source
Redshift is currently being used to house normalized client data pulled from various third-party endpoints. It houses the data that is both being accessed directly by our business intelligence and CRM platform, as well as made available via our own API gateways. It was chosen for its ability to support a "big data" environment with high availability.
  • If you need draw insights from immense amounts (see: petabytes) of transactional (repetitive) data in near real time--think machine learning and business intelligence--and you're already in the AWS ecosystem, then it's your only real option. It performs very well.
  • Highly configurable, intelligent compression of repetitive columns reduces your memory footprint, lending to extremely high performance.
  • As with most things in the AWS ecosystem, it scales seamlessly and endlessly.
  • There is no support for data de-duplication; meaning this has to be either accounted for upstream, or you'll have to build your own services to de-dupe your data.
  • It's strength is housing data, not necessarily data insertions. While it has an SQL-like interface, it shouldn't be approached the same as a typical relational database.
  • Permissions can be a pain... dovetailing on my previous "con" , in some instances it's easier to drop/rebuild a table than try to navigate incremental updates/insertions, but retaining user-permissions is a pain-point.
It is well suited for:
  • Petabytes of data requiring near real-time analysis
  • Massive Data Insertions
  • Massive Data Reads
It isn't well suited for:
  • Web apps
  • Smaller transactional inserts
  • Smaller reads
You wouldn't drive an 18-wheeler to the corner store to pick up a bag of chips. Your specific need will determine whether or not Redshift is suited for the job.
Read Kyle Reichelt's full review
No photo available
December 19, 2018

Review: "Amazon Redshift - a new generation of data warehouse platform"

Score 8 out of 10
Vetted Review
Verified User
Review Source
Amazon Redshift is being used as the main data warehouse across the whole organization. It provides essential business performance data and valuable intelligence. Different departments utilize it for a variety of operations, and decision makers rely on the data analytic tools built upon its data for tracking and enhancement, wherever needed.
  • It does very well in data ingestion, and compresses data efficiently.
  • Most of the queries return results quickly even with large data sets.
  • It has a hard limit of total number of concurrent connections to the database. Compared with conventional databases that limit is very low.
  • Its workload management (WLM) mechanism could be improved, such as made more dynamic and easier to tune and manage.
It is best for large structured data sets with data normally loaded from files, instead of through heavy SQL data manipulation. If there are SQL data manipulation, better to have mainly DDL (create/drop/alter) instead of DML (insert/update/delete) to achieve the same goals. It is also better for smaller number of concurrent connections (less than 500).
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Tamás Imre profile photo
February 06, 2018

Review: "Amazon Redshift: a fast, scalable and easy to use data warehouse service"

Score 9 out of 10
Vetted Review
Verified User
Review Source
We use Amazon Redshift as our data warehouse. We store our most used datasets on Redshift. Some of the data transformations are also made on Redshift but we try to avoid to do the heavy transformation there. Our dashboarding tool sits on top of Redshift and runs queries on every dashboard load. Analysts are using Redshift all day as the main source of data. Data savvy engineers and product managers have read access.
  • It is easy to use. SQL is one of the easiest languages to learn.
  • Fast. Especially if you use an SSD cluster.
  • Scalable. If you need more space for your data or want faster results you can add more nodes.
  • It is PostgreSQL. I miss some commands and procedural features.
  • It is perfect for analytics purposes, but not fast enough for web apps.
  • Too expensive for ETL processing.
Redshift is a great data warehouse. It can serve the analyst team and most BI tools perfectly. Easy to learn and use. If you are using AWS then Amazon Redshift is a self-evident choice. You can use Redshift for ETL but It can be too expensive. It is not appropriate to serve as an in-production DB.
Read Tamás Imre's full review
Yogen Sanghani profile photo
February 01, 2018

Amazon Redshift Review: "On-prem DW to Redshift"

Score 10 out of 10
Vetted Review
Reseller
Review Source
Amazon Redshift is being used by several of our clients for analysis of large datasets. In most cases, it is used at a department level, in conjunction with other on-prem and in the cloud data solutions, including data warehouses and relational databases.
  • Very fast, parallelized data loading from S3
  • Full ANSI SQL support
  • Highly scalable
  • Columnar storage
  • Does not scale automatically. Need to be scaled up/down manually by adding/removing nodes
  • Does not have support for row level access control
  • Charged based on provisioned capacity - not based on usage
Redshift is well suited as an alternative to on-prem data warehouses. AWS Data Migration Services can be used to migrate data from various relational databases into Redshift.
Read Yogen Sanghani's full review
Vinaybabu Raghunandha Naidu profile photo
October 12, 2017

Amazon Redshift Review: "Scalable and cost-effective data warehousing tool"

Score 7 out of 10
Vetted Review
Verified User
Review Source
Redshift is one of the most scalable yet simple data warehousing tools. It is not only used within the engineering team but widely used by the business team too. One of the major use cases are to power the dashboards and reports which are in looker. It helped to migrate from the traditional email reports to dynamic dashboards and reports which can be shared to various end users and businesses. It helped in faster data retrieval and can scale well with ease.
  • Scalability with less downtime.
  • Performance optimization without affecting the business i.e zero downtime during optimization
  • Powerful yet simple to use. Very easy to optimize and improve performance during the regular read/write operations
  • No need of DBA to operate and maintain Redshift as it is a completely managed data warehousing tool
  • Good customer support and will respond in very quick time with clear information and documentation
  • Well documented commands
  • Can propose better optimization techniques based on the business use case.
  • Can provide option to set the upper bound on number of connections to cluster
  • Can improve on optimized writes/updates
Well suited for faster data retrieval and powering OLTP need and perfectly suited for generating dynamic dashboards and reports.
Not suited for massive data storage and analytics. Can-not handle unstructured data.
Read Vinaybabu Raghunandha Naidu's full review
No photo available
November 21, 2017

Amazon Redshift Review: "Best Analytics Cloud Data Warehouse Database"

Score 10 out of 10
Vetted Review
Verified User
Review Source
We used the Amazon Redshift for Analytics Data Warehousing. It helped to process our various departments in organization like renewals, sales, marketing & finance department to analyze the data very quickly and performance effective with tableau reporting tool.
  • It's a columnar data storage architecture and which allows it to particularly run structured data query for reporting very fast.
  • We used amazon redshift cloud datawarehouse with Tableau, looker reporting tool and it has perfectly helped our reporting needs for business users.
  • Very easy to copy data from Amazon Web Services S3 storage container to Redshift Database with simple copy statements.
  • It provides built-in commands to table structure effectively with less use of memory.
  • AWS can provide some cheaper options with pre core cpu purchase rather than hourly charges on amazon redshift.
  • There are no options for on-premise set-up of the amazon redshift database.
  • Limited documentation on best practices for dist key, sort key and various amazon redshift specific commands.
It's the best option when we need to have a high volume of structured data analytics datawarehouse design & development. It perfectly reports fast with tableau reporting tool, even data around 300 million records. It's best suited where the organization is planning to build a custom datawarehouse rather than using any pre-packaged BI Apps data model.
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Michael Romm profile photo
September 13, 2017

Amazon Redshift Review: "After Four Months with Redshift"

Score 6 out of 10
Vetted Review
Verified User
Review Source
Amazon Redshift is being used by many business units within our company. It is our new data warehousing platform.
  • Redshift seems to be as fast processing a large dataset as it is with a small one. It seems, when the dataset size is significantly increased (10x, 100x, 1000x, etc.), DML queries are often executed within the same amount of time.
  • Redshift has a powerful graphical admin tool to monitor the ongoing queries in real time and historically.
  • Easily expandable capacity. Automatic snapshots that eliminate the need for managing backups. Simple database maintenance strategies with the VACUUM and ANALYZE commands.
  • Abundance of detailed documentation and tutorials.
  • It could benefit from adding data integrity and programming tools common to other database management systems.
  • Amazon Redshift is based on PostgreSQL 8.0.2. That version of PostgreSQL was released in December 2006. While PostgreSQL was much improved since then, the new features were not implemented in Redshift. Many basic features are missing from it.
  • Primary keys can be declared but not enforced. Referential integrity (foreign keys) can be declared but not enforced. UNIQUE and CHECK constraints are not supported and cannot be declared.
  • IDENTITY can be declared on a column, and Redshift will put unique values into it. However: IDENTITY values in the newly inserted rows won’t be incremental or sequential. To implement a sequential number, you need to write your own custom code.
  • There are no stored procedures in Redshift. We are writing SQL script files, and then parsing and running them one statement at a time from a Python program. This also enabled us to implement execution-time error logging.
  • In SQL scripts, to check for the row count of affected rows, a complicated join query against some system tables or views has to be executed.
  • Data Control Language (DCL) does not exist. No statements like IF, WHILE, DO, RAISERROR, etc.
  • On performance of views… Views do not “pass-through” a query parameter which is a potential problem for performance.
  • When selecting against a view with the WHERE clause outside of the view, the inner query of the view will be executed first without consideration for the WHERE clause, and only then the WHERE clause will be applied.
  • Certain clauses of SQL work many times faster than other clauses. So be careful and test your statements for performance earlier rather than later, especially if working with a large data set.
  • There was a situation when DELETE FROM JOIN was unacceptably slow. Replacing JOIN with the USING clause made DELETE instantaneous.
Redshift is a viable platform to house a large or very large data warehouse designed for performance and scalability. It is especially well-suited in the cases where your source data is already stored inside of the AWS services infrastructure.
Read Michael Romm's full review
No photo available
June 23, 2017

Review: "Amazon Redshift is a widely used tool, and a good choice for many organizations."

Score 9 out of 10
Vetted Review
Verified User
Review Source
My company uses Amazon redshift to store the majority of our website and offline data. We connect Redshift to dashboard/visualization tools that are used by the entire company. Data availability and ad box analysis is very important to my organization, so having a reliable and accessible storage system is vital.
  • It is built out and widely used, which makes it easier to debug and learn.
  • Cheap to store and query.
  • PostgreSQL makes it easier to query.
  • We'd like to get streaming live data.
  • Compute and storage are connected, which can waste CPU.
  • Loading the data into Redshift can be challenging.
If you are using Google Analytics 360, then Googel BigQuery would be more appropriate. However for companies with big transactional data that want to do complex SQL querying--it could be a good choice.
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Seth Goldberg profile photo
August 01, 2016

Amazon Redshift Review: "Great data warehouse for the money!"

Score 8 out of 10
Vetted Review
Verified User
Review Source
Amazon Redshift is used as the central data warehouse. It's main use is for data analytics and reporting. In addition, it is also used by batch jobs to perform various business functions like email lists of delinquent customers.
  • Fast analytical queries. The shared nothing and column oriented architecture makes querying very quick compared to databases like Oracle that are designed for OLTP. Scaling is a synch since you can scale out by adding more nodes.
  • Easy table modelling. The only tough decisions you have to make are what your distribution schemes and sort keys are going to be. This is a lot easier than defining partition and index schemes in databases like Oracle or MySQL.
  • Not much maintenance. Almost everything is managed by Amazon. The only exception is table vacuuming and analysis. I was able to program simple ETL jobs to perform this.
  • Works with pretty much anything that works with Postgres. It's hard to find a tool that it isn't compatible with.
  • Lack of enforced constraints (except NOT NULL column constraints). You have to be very careful in your testing to make sure that you aren't duplicating rows.
  • No stored procedure support. Everything must be accomplished through ETL
  • Write operations are very slow and complex.Native SQL row level INSERT and UPDATE statements take an extremely long time to execute. In order to get around this for external data that needs to be loaded, you have to bulk load the data from a flat file to a stage table, then upsert the data from the stage table to your destination table. For data already present in the database, ELT is the only viable way of transforming the data.
  • No good native data modelling tools.
  • Random nondescript errors happen occasionally. The error messages are not decipherable and forums will have no clues as to what happened. It is just a fact of life.
  • No trigger support.
  • OLTP style queries are painfully slow. Don't even think about using Redshift for OLTP...
For data warehousing and analytics, Redshift can't be beat. It's price point, minimal maintenance, and OLAP query optimization make it excellent for querying and reporting for an organization with a small budget. As long as you can live without some standard database tools like constraints and stored procedures, it is an excellent database.
Read Seth Goldberg's full review
No photo available
August 10, 2016

Amazon Redshift: "Redshift review for the analytics environment"

Score 8 out of 10
Vetted Review
Verified User
Review Source
It is used as the analytics data SOT (source of truth). All company data, whether from product, marketing, sales, etc., gets synced to Redshift where it can be easily analyzed by analysts. Redshift provides a good tradeoff between the ability to store a lot of data and perform quick and flexible queries on it.
  • Flexible, OLAP queries.
  • Fast query time (in the order of seconds for most).
  • Standard SQL language.
  • Fast ways to insert more data.
  • VACUUM is a pain, its unclear exactly how often it needs to be done.
  • Redshift has a limit on how many concurrent writes and reads you can do that won't scale to 100s of people using it.
  • Redshift lacks some Postgres queries that make some standard SQL operations hard.
It's well suited to be used in an analytics environment where the consumers are 1-50 analysts who need to write complex queries against the data, where total data size is in the 1TB-1000TB range, and where there's no need for data latencies less than one hour. It won't work well in the PB scale, where there are too many consumers and data producers, and for real time applications.
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No photo available
August 05, 2016

Amazon Redshift Review: "get started with columnar databases easily with AWS Redshift"

Score 7 out of 10
Vetted Review
Verified User
Review Source
(JSON) events emitted from platform/web services are transformed and loaded into AWS Redshift in order to support analysis and reporting for our solution.
  • AWS infrastructure and support simplifies maintenance and administration
  • familiarity with PostgreSQL makes adopting Redshift as a column store easier
  • columnar data store allows for high performance queries on large volumes of data
  • there are some situations where having a column store more closely integrated as part of our platform would be better
  • AWS costs can add up
  • many other (open source) column stores have new and interesting features not (yet) available in Redshift
If you want an easy way to get started with a column store, spin one up on AWS and see if it fits your use case. AWS is a reasonably cheap way to adopt new technologies. Then after a while, you'll be in a better position to decide whether to commit more to AWS or choose from comparable technologies available.
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About Amazon Redshift

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
Categories:  Data Warehouse

Amazon Redshift Technical Details

Operating Systems: Unspecified
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