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 HackelingGreat for "Big Data" -- but not so much otherwise2018-11-26T00:40:00.145ZRedshift 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.Kyle ReicheltAmazon Redshift - a new generation of data warehouse platform2018-12-19T19:57:17.408ZAmazon 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 ServiceVerified UserAmazon 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 Sanghani
Unspecified
Amazon Redshift
88 Ratings
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