Skip to main content
TrustRadius
Amazon Redshift

Amazon Redshift

Overview

What is Amazon Redshift?

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

Read more
Recent Reviews

Redshift trumped Hive

9 out of 10
January 15, 2021
Incentivized
It is used within a few departments. It is used to solve certain legacy problems that have not yet been ported over to other more suitable …
Continue reading
Read all reviews

Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Return to navigation

Pricing

View all pricing

Redshift Managed Storage

$0.24

Cloud
per GB per month

Current Generation

$0.25 - $13.04

Cloud
per hour

Previous Generation

$0.25 - $4.08

Cloud
per hour

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
Return to navigation

Product Demos

ETL From Amazon RDS to Amazon Redshift with using AWS Glue Service

YouTube

Introduction to Query Scheduler for Amazon Redshift

YouTube

ETL From AWS S3 to Amazon Redshift with AWS Lambda dynamically.

YouTube

Amazon Redshift Tutorial | AWS Tutorial for Beginners | AWS Certification Training | Edureka

YouTube
Return to navigation

Product Details

What is Amazon Redshift?

Amazon Redshift Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

Reviewers rate Usability highest, with a score of 10.

The most common users of Amazon Redshift are from Mid-sized Companies (51-1,000 employees).
Return to navigation

Comparisons

View all alternatives
Return to navigation

Reviews and Ratings

(207)

Attribute Ratings

Reviews

(1-17 of 17)
Companies can't remove reviews or game the system. Here's why
Dileep Kumar | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
I use it as the data warehouse of our clients. I use it to build data transformations of user activity logs to ML features. I use the sql workbench to explore datasets and understand data schemas. Post that, I generally connect to the warehouse either through dbt or from jupyter notebooks.
  • Seamlessly integrates with the data in s3
  • Workbench provides useful way to query the tables within aws console
  • Postgres flavor of sql gives powerful capabilities such as window functions
  • Json support in sql is very limited.
  • Array type columns are missing. They are by default converted to strings
  • Sql workbench often goes unresponsive. I have to reload for the queries to run
  • A search option in the sql workbench would be great, which let's users search the whole db for a match on columns, tables etc
It is a solid data warehouse on top of the AWS ecosystem. If most of your infra is on AWS, it makes good sense to go for it. But it is expected to be tuned well by a data engineer for an optimal performance. For a data scientist too, the SQL is a bit limited when it comes to unstructured columns in the tables. Arrays, jsons, etc have very poor support compared to other warehouses.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Amazon Redshift is our Data Warehouse, where we store our processed data (Hot data) for various initiatives like BI, Analytics, DataScience, etc

We also use Amazon Redshift Spectrum as our Data Lake, where we store raw (un-processed) data (Cold data) for historical analysis, trends, etc

We store various standard data in Redshift like:
Bronze (ETL-ed data),
Silver (Materialized Views data), and
Gold (Rollups/Aggregated/Dashboard-ready data) in [Amazon] Redshift





  • [Amazon] Redshift has Distribution Keys. If you correctly define them on your tables, it improves Query performance. For instance, we can define Mapping/Meta-data tables with Distribution-All Key, so that it gets replicated across all the nodes, for fast joins and fast query results.
  • [Amazon] Redshift has Sort Keys. If you correctly define them on your tables along with above Distribution Keys, it further improves your Query performance. It also has Composite Sort Keys and Interleaved Sort Keys, to support various use cases
  • [Amazon] Redshift is forked out of PostgreSQL DB, and then AWS added "MPP" (Massively Parallel Processing) and "Column Oriented" concepts to it, to make it a powerful data store.
  • [Amazon] Redshift has "Analyze" operation that could be performed on tables, which will update the stats of the table in leader node. This is sort of a ledger about which data is stored in which node and which partition with in a node. Up to date stats improves Query performance.
  • Amazon Redshift is a Managed Service. But it is Not a 100% managed service. We still need to configure it with WLM (Work Load Management) settings, and add Query Queues to make sure it's resources aren't wasted and it is performant at it's best state, all the time
  • [Amazon] Redshift has a concept of "Vacuum", which is an operation to claim the disk space back from deleted data/tables. They recently started doing automated vacuuming. Prior to that we had to do that at regular intervals, to claim the data back.
[Amazon] Redshift is suited for various use cases like Time series data, Structured / relational data, Semi structured data like JSON, etc.

[Amazon] Redshift might not work 100% well with full performance, for Graph DB use cases.
Arthur Zubarev | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Amazon Redshift is a PostgreSQL based solution was seen as a drop-in replacement for several Postgres based databases (or schemas in Postgres parlance).
The eventual product: a Bill Inmon principles-based Data Warehouse served as a point or source of a single truth. It aided in decision making, historical outlooks and forecasting across various organizational verticals - the Finance, Marketing, and Medical Research. It was also possible to deliver data extracts to 3rd parties or visualize data on demand.
  • Data retrieval experience really gets improved.
  • In terms of database management, it is really a no management at all in AWS. There is no even an OS to take care or worry about.
  • Auto or on-demand scaling is nice.
  • Integrates quite well with other products within the AWS ecosystem.
  • The number of connections is too small, I think at around 50 are allowed in parallel. With some ETL and apps connecting all the time, this brings an undesired possibility to some users or tools being unable to connect.
  • Needs some tuning.
  • The logging part is almost nonexistent.
  • Can be quite costly in the long run as opposed to just RDS or on-prem/dedicated solutions.
If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for).
January 15, 2021

Redshift trumped Hive

Score 9 out of 10
Vetted Review
Verified User
Incentivized
It is used within a few departments. It is used to solve certain legacy problems that have not yet been ported over to other more suitable approaches.
  • Large scale SQL
  • Standard SQL
  • Handline full text queries
  • Sampling
  • Bonafide indexes
  • Provide query interface that can store queries and run long-running queries, then notify the user
It's appropriate for ad hoc queries on semiorganized data.
December 30, 2020

Redshift Review for you!

Duncan Hernandez | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User
Incentivized
Amazon Redshift is being used by our whole organization. It is our primary tool for our data warehouse. We decided to switch to a cloud database because our in house servers just weren't able to keep up with our need for fast data delivery. We can adjust the speed up to where we need it to be and it has been very useful.
  • Aggregation
  • Extracting data
  • postgres Based
  • Could be faster
  • Limited sql workbenches
  • Expensive when speeding up the processing
Redshift is best suited for our data storage and designing our fact and dimension tables. We keep our non-structured data there that can be accessed at any time as well as our relational database. I'd say that if you don't have a need for a relational databasae, then Redshift probably isn't going to be a viable product.
November 30, 2019

Redshift Review

Score 2 out of 10
Vetted Review
Verified User
Incentivized
Amazon Redshift was our enterprise data warehouse as a backend to our BI solutions.
  • Fixed cost.
  • Tunable table design.
  • Need to provision warehouse for highest capacity.
  • No real separation between computing and storage (even when considering Spectrum).
  • All users share the same infrastructure resulting in frequent 100% utilization error messages.
  • A leader node can become a bottleneck for too many concurrent aggregate queries.
Redshift is appropriate when the number of concurrent users are low and pointed queries are the focus. It is not appropriate when a large number of concurrent users is to be supported,
Score 7 out of 10
Vetted Review
Verified User
Incentivized
AWS Redshift is the cloud-based data warehouse where we store our application level datasets and is used further for insights from the stored dataset. It improves the decision support for our business based on data analytics on a large set of real-time datasets which force the business processes to the next level. It provides good performance with high availability for essential data analysis and valuable intelligence.
  • Easy query and fast execution
  • High performance and availability
  • Support of large datasets
  • Scalable solution
  • Database optimization
  • Time consuming process for schema design and modification
  • Integration is little bit difficult
Amazon Redshift is the data warehouse under the umbrella of AWS services, so if your application is functioning under the AWS, Redshift is the best solution for this. For large amounts of data, the application is the best fit for real-time insight from the data and added decision capability for growing businesses. If your application is outside of AWS it might add more time in data management.
Vibhakar Prasad | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
1)ETL(Talend) data from source applications (Salesforce, Jira, OpenAir, NetSuite, Sharepoint, Active Directory, Office 365, etc.) to S3 bucket and from S3 bucket push data to Redshift (sync time is 10 minutes intervals. Data is available almost real-time only lagging 10 minutes).
2) All Department use it—Engineering, Sales, and Marketing.
3) As I said data is almost in real-time, so it is very useful for taking real-time decisions for upper management. We also reduced Salesforce licenses, because most of the users only used it to see reports. Now they are happy to used Redshift.
  • We reduced the number of Salesforce licenses— Engineering, Sales and Marketing guys are happy to query data from Redshift.
  • Very fast to provide a huge data set with complicated measure.
  • Some of the calculations failed in Salesforce. Redshift returns with the same calculations very fast.
  • Very easy to maintain, no need to worry about hardware failure.
  • We are not able to modify column size.
I recommend all to use Redshift, It is easy to use and maintain. We have reduced the number of Salesforce licenses due to real-time data we have in Redshift. People are happy to use Redshift.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Redshift is being used by engineering for our data warehouse or data lake, if you will. It's part of our ETL pipeline, where the data is used to form dashboards and analytical queries across all of our initially segregated data. So it is kind of a source of truth linking data across the company. These dashboards are accessible across other departments in the organization. The data is consumed by everyone, not just engineers.
  • It's fast for data analytics across multiple columns.
  • Essentially, it's good for big datasets.
  • By using RedShift you're kind of married to using AWS's other services, e.g. Redash.
  • You need your data in the cloud.
  • No separate storage and computing.
  • No structured data types.
  • Doesn't scale easily.
Use Redshift for data warehouses, especially if your data is already in the cloud (AWS). It's great for large datasets, and fast too, even if your dataset is column heavy. It's less so for when you have a bunch of rows. All in all, it's a good starting point for any aspiring data warehouse, but there are other promising solutions too. E.g. Snowflake.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use it as our data warehouse for reporting purposes.

My company used it solely for reducing the performance overhead of running long SQL queries. The seamless implementation of Redshift allowed us to get the data ready to go for our customers to run the reports they need. It is currently used by a few customers, but we are trying to get each of our customers to use this rather than using the traditional OLTP database.
  • Easy to work with
  • Seamless implementation with matillion
  • Massive data reads and inserts
  • I didn't like the security aspect of this where it asks us to create views for each customer.
  • It does not support row-level controls.
  • Some SQL queries are faster on native SQL than here. But it could be the data conversions that is causing it.
[It's] very good for reports and dashboards and is easy to use and fast performance makes it a good choice. It excels in columnar architecture and aggregates. If you have many clients as we do, you need to have separate schemas for each of them. That is a good way but also there is too much clutter spread all over. That is the only drawback I see, end users can see data in the schema, there are no individual permissions allotted.
Brendan McKenna | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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).
Tamás Imre | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Vinaybabu Raghunandha Naidu | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
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.
Score 7 out of 10
Vetted Review
Verified User
Incentivized
(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.
Seth Goldberg | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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.
Return to navigation