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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.https://media.trustradius.com/product-logos/ri/0H/K93ECPX8DWIX.pngAmazon Redshift ReviewAmazon 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.,8,Redshift as a solution is viable in case an enterprise wants a relatively uncomplicated relational data storage engine that is elastic and autonomous. The costs are reasonable compared to other storage systems provided Redshift is capable to grow with your business and reduce administrative costs. Redshift integrates well with many of the offerings within AWS and outside it. The programmability story is also good.,Amazon Aurora, Snowflake and Azure SQL Database,7,Amazon Aurora, Azure SQL DatabaseAmazon RedShift is great if you have multiple data channel sourcesAmazon RedShift is used in all departments and accounts. This tool is newly integrated into the system so as to work with the data on the cloud. There are various projects which are moving from SQL servers to ARS because of its capacity of working and managing the data in the cloud.,We can connect with multiple servers and can fetch the data easily from one server to other. It supports the syntax of the bots of the SQL servers, MS SQL and Oracle SQl. This makes it pretty handy to use. Here we use views instead of tables, so we can clearly see the flow of data.,It works very slow in the cloud environment. No statistical inbuilt functions are available within the tool. Its user interface is not very attractive. Often it goes into deadlock state, which kills the running jobs.,8,As of now, there are no results on the ROI, as we have just deployed this tool about a year ago. But this is a good tool to use as it is the advanced version of the data reporting platform. With the help of Amazon RedShift, we can properly use our server spaces which in result gives a proper ROI as with the limited storage capacity we can store enough data. It tends to work very slow, so it reduces the efficiency of the employees who are using it. Hence this is the major challenge with the employees.,Microsoft SQL Server and PostgreSQL,Microsoft SQL Server, SAS Enterprise Guide, Devart Excel Add-ins,9Redshift is awesome!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.,10,I have not found any negative impact.,Google BigQuery,Talend Data Integration, Tableau Desktop, Sisense, Business Objects SRC,10Amazon Redshift: easy, simple, fastCapital One has many LOBs (line of businesses). I have supported IAM and Commercial LOB. They are using Redshift as a data warehouse solution. Oracle is not a Data Warehouse solution but was being used when the application was on on-premises. When they wanted to migrate all data to the Cloud, they chose Redshift as a solution to move the data from Oracle. Oracle is not a data warehouse solution. Redshift has been found as a good solution because of its unique features such as its MPP architecture, columnar architecture, and storage capacity.,User-experience. The user wants something quick to view the output, rather than spending too much time preparing a code prior to seeing the output. Redshift provides SQL type queries. This makes any user happy and comfortable. Architecture is very straightforward and simple to understand, such as MPP architecture, Encryption, and Columnar database design. We can easily address issues and help others to understand. Scalability. We can scale-up and scale-down based on our workloads. Performance tuning and database optimization can be done using the system tables and advisors. These solutions are similar to the solution available for Oracle SQL Server. It makes it easy to do the optimization for queries and databases.,The concurrency and scale up based on it could be improved. It would be good if it scale-up and scale-down the memory/CPU capacity automatically based on workload. Often we experience slow on queries and dashboards. Self-tuning option in WLM does help. Optimizing the areas such as Vacuum and reorganize the column data (sorting over time) automatically.,8,As long as you have the perfect designer and architect, you will receive the expected benefit from Amazon Redshift.,Snowflake,PostgreSQL, Snowflake,8Redshift is way too easy!Redshift is our data warehouse used by our organization. It takes data from different sources and put them together in Redshift for our Analytics team to diagnose.,Since it's part of AWS it is fairly quick and easy to set up. You can add nodes fairly quick to expand the data needs. Performance from the analytics reports accessing Redshift is really good.,Better database management when looking up table metadata or sizes of tables. Need a better query analyzer. Finding errors during a data load can be a little daunting at times.,9,Cheaper cost to run. Readily available and can easily expand. Part of the AWS echo system which is great, because our Analytic tools are also part of AWS.,PostgreSQL,5,AWS CodePipeline, Red Hat JBoss Enterprise Application Platform, Oracle Database 12cAmazon Redshift: Reliable and powerful cloud-based data warehouseAWS 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,7,Realtime and high availability, add quick access of the data. Improve decision support based on quick data analytics. Costlier compared to others, but we can generate ROI within a year, based on large scale applications.,Azure SQL Database, Google Cloud SQL and Google BigQuery,Google BigQuery, Azure SQL Database, Tableau Server,7Start a data warehouse today with Redshift!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.,8,Very positive for what it has done, providing a way to aggregate data sources. It allows marketing to pull statistics across the consumer base that would not have been otherwise possible. It allows actual analytics across the company, in an easily accessible manner.,Snowflake,Amazon S3 (Simple Storage Service), Terraform, Kubernetes,7Great Analytic Data-WarehouseAmazon Redshift is being used as our primary analytic data-warehouse. This allows our data and analytic team to build report and query data without going directly to our production database. It is a central data repository from external data sources as well, data we import from 3rd parties and segment.,Complex queries Aggregation Fully managed service Works very well with most BI/reporting solutions,Stored procedures Job scheduling A easier way (perhaps a GUI) to manage users permission,10,Easy to set up and very cost effective to run large complex queries. Separation of roles between our production and analytic environment. A lot of cloud service is RedShift ready (i.e., Segment, FiveTran, Matillion) which decreases implementation efforts,Snowflake, Google BigQuery, Microsoft SQL Server and Amazon Relational Database Service,Tableau Desktop, Tableau Server, Matillion, AWS Lambda,9smooth implementation and Quicker reports/dashboardsWe 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.,9,A client was unhappy and complained about slowness. Once we explained and showed them the new tool, they became very happy and stayed. A faster and easy approach to OLAP Schema allowed us to think out of the box.,9Amazon 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,10Great 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 ServiceA 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 LambdaAmazon 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, ChartioOn-prem DW to RedshiftAmazon 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,10,Our clients have achieved great ROI by migrating all or parts of their data from expensive data warehouse hardware solutions to Redshift. Some clients are seeing increasing costs of Redshift, as they are being charged for provisioned capacity, and are looking at alternative solutions which provide pay per use options.,Google BigQuery,MySQL, Oracle Database, SQL Server Integration ServicesBest Analytics Cloud Data Warehouse DatabaseWe 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.,10,It provides great returns due to its fast processing data analytics purpose. It sometimes toss for cost of AWS Service with Amazon Redshift.,Oracle Database as a Service,Oracle Database as a Service, Cloudera EnterpriseAfter Four Months with RedshiftAmazon 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.,6,Our company is moving to the AWS infrastructure, and in this context moving the warehouse environments to Redshift sounds logical regardless of the cost. Development organizations have to operate in the Dev/Ops mode where they build and support their apps at the same time. Hard to estimate the overall ROI of moving to Redshift from my position. However, running Redshift seems to be inexpensive compared to all the licensing and hardware costs we had on our RDBMS platform before Redshift.,,AWS Lambda, Microsoft SQL Server, ER/StudioScalable and cost-effective data warehousing toolRedshift 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,7,Increased the end users as the reports and dashboards was very helpful to give a clear insights of business Increased business as the analysts were able to retrieve the data in quick time Improved the data retrieval as most are familiar with SQL,,Teradata Database, Treasure Data, MongoDBGreat data warehouse for the money!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...,8,Redshift has had a very positive impact on our business. It has been used to provide analytics on marketing campaigns to boost revenue. Redshift is instrumental in our payment collection business processes. It powers everything from who gets called to who gets sent collection emails.,Oracle Data Warehouse, MySQL and Teradata Data Warehouse Appliance,MySQL, Tableau DesktopAmazon Redshift is a widely used tool, and a good choice for many organizations.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.,9,Redshift has played an important role in allowing our company to share data and insights across the company. Saved us money on data storage. The SQL nature made it faster for our team to implement and use.,,Google Analytics, Looker, Tableau DesktopRedshift review for the analytics environmentIt 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.,8,Allowed us to easily analyze business operations and facilitate A/B testing. Allowed us to quickly answer complex questions about the company's data. Took a lot of work to fix some issues once we got to the limits of its usability (specifically, we had too many writes).,Vertica, Google BigQuery and Apache Hive,Apache Spark, Apache Hive, Verticaget started with columnar databases easily with AWS Redshift(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,7,Redshift has enabled us to expose more of our product and business performance data which was previously not accessible or reportable. We can now make data driven decisions for our business.,,PostgreSQL, Redis
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Amazon Redshift
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Amazon Redshift
128 Ratings
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Score 8.4 out of 101

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Arthur Zubarev profile photo
October 10, 2019

Amazon Redshift Review

Score 8 out of 10
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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).
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Akshaya Bhardwaj profile photo
Score 8 out of 10
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Amazon RedShift is used in all departments and accounts. This tool is newly integrated into the system so as to work with the data on the cloud. There are various projects which are moving from SQL servers to ARS because of its capacity of working and managing the data in the cloud.
  • We can connect with multiple servers and can fetch the data easily from one server to other.
  • It supports the syntax of the bots of the SQL servers, MS SQL and Oracle SQl. This makes it pretty handy to use.
  • Here we use views instead of tables, so we can clearly see the flow of data.
  • It works very slow in the cloud environment.
  • No statistical inbuilt functions are available within the tool.
  • Its user interface is not very attractive.
  • Often it goes into deadlock state, which kills the running jobs.
Amazon Redshift is well suited for fetching data from multiple different sources and servers, and it is very easy to learn how to use this tool. It is less appropriate for situations where you may need to process only limited rows, as this takes a large amount of time since you cannot create multiple tables in it.
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Vibhakar Prasad profile photo
Score 10 out of 10
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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.
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Jacob Biguvu profile photo
Score 8 out of 10
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Capital One has many LOBs (line of businesses). I have supported IAM and Commercial LOB. They are using Redshift as a data warehouse solution. Oracle is not a Data Warehouse solution but was being used when the application was on on-premises. When they wanted to migrate all data to the Cloud, they chose Redshift as a solution to move the data from Oracle. Oracle is not a data warehouse solution. Redshift has been found as a good solution because of its unique features such as its MPP architecture, columnar architecture, and storage capacity.
  • User-experience. The user wants something quick to view the output, rather than spending too much time
  • preparing a code prior to seeing the output. Redshift provides SQL type queries. This makes any user happy and comfortable.
  • Architecture is very straightforward and simple to understand, such as MPP architecture, Encryption, and Columnar database design. We can easily address issues and help others to understand.
  • Scalability. We can scale-up and scale-down based on our workloads.
  • Performance tuning and database optimization can be done using the system tables and advisors. These solutions are similar to the solution available for Oracle SQL Server. It makes it easy to do the optimization for queries and databases.
  • The concurrency and scale up based on it could be improved. It would be good if it scale-up and scale-down the memory/CPU capacity automatically based on workload.
  • Often we experience slow on queries and dashboards. Self-tuning option in WLM does help.
  • Optimizing the areas such as Vacuum and reorganize the column data (sorting over time) automatically.
Amazon Redshift is good for when you need a Data Warehouse solution or a user-experience such as SQL kind queries. It is also good if you have limited budget constraints. It's not suggested when someone has DML queries such as INSERT, UPDATE, DELETE.
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September 27, 2019

Redshift is way too easy!

Score 9 out of 10
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Redshift is our data warehouse used by our organization. It takes data from different sources and put them together in Redshift for our Analytics team to diagnose.
  • Since it's part of AWS it is fairly quick and easy to set up.
  • You can add nodes fairly quick to expand the data needs.
  • Performance from the analytics reports accessing Redshift is really good.
  • Better database management when looking up table metadata or sizes of tables.
  • Need a better query analyzer.
  • Finding errors during a data load can be a little daunting at times.
It's very cost-effective from other databases we were using for our data warehouse. It was really easy to set up and it used our ETL tools to migrate data from different data sources. We added functionality add aggregate the data set for our Analytics team to analyze.
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Score 7 out of 10
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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.
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Score 8 out of 10
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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.
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Score 10 out of 10
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Amazon Redshift is being used as our primary analytic data-warehouse. This allows our data and analytic team to build report and query data without going directly to our production database. It is a central data repository from external data sources as well, data we import from 3rd parties and segment.
  • Complex queries
  • Aggregation
  • Fully managed service
  • Works very well with most BI/reporting solutions
  • Stored procedures
  • Job scheduling
  • A easier way (perhaps a GUI) to manage users permission
Amazon Redshift is great for analytics, reporting, and complex queries for statistical modeling and machine learning. Its ability to run parallel queries in a simple SQL environment makes the transition from traditional DB very easy. Very good for loading/reading/writing large datasets. I would not recommend RedShift for an environment that requires single row reads/updates, which it is not optimized for.
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Score 9 out of 10
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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.
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Score 9 out of 10
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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.
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Kyle Reichelt profile photo
Score 7 out of 10
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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
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Score 8 out of 10
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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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Score 8 out of 10
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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.
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Tamás Imre profile photo
Score 9 out of 10
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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.
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February 01, 2018

On-prem DW to Redshift

Score 10 out of 10
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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.
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Score 10 out of 10
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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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Score 6 out of 10
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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.
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Vinaybabu Raghunandha Naidu profile photo
Score 7 out of 10
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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
Seth Goldberg profile photo
Score 8 out of 10
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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
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Score 9 out of 10
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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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Score 8 out of 10
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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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Score 7 out of 10
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(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

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