TrustRadius
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.https://dudodiprj2sv7.cloudfront.net/product-logos/ri/0H/K93ECPX8DWIX.pngAmazon AWSA hands-free data warehouse2018-10-12T17:54:38.680ZWe 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 LambdaGavin HackelingAmazon Redshift: a fast, scalable and easy to use data warehouse service2018-02-06T16:10:59.742ZWe 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, ChartioTamás ImreOn-prem DW to Redshift2018-02-01T22:08:56.220ZAmazon 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 ServicesYogen SanghaniBest Analytics Cloud Data Warehouse Database2017-11-21T19:01:09.677ZWe 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 EnterpriseVerified UserAfter Four Months with Redshift2017-09-13T17:33:56.721ZAmazon 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/StudioMichael Romm
Unspecified
Amazon Redshift
86 Ratings
