We use Amazon Redshift for structured data warehousing. It allows us to store, retrieve, and analyze large volumes of structured data quickly and efficiently. It is used to support decision-making, identify trends, and gain insights into the business. Furthermore, we use Amazon Redshift can be used to create dashboards, generate reports, and perform ad-hoc queries on data to support business intelligence and analytics efforts. We also use it to support our customer service applications or fraud detection systems
Pros
Data warehousing
Business intelligence
Data insights
Cons
Cost can be prohibitive
User interface could be more intuitive
Likelihood to Recommend
Amazon Redshift is well-suited for a variety of scenarios where businesses need to store, retrieve, and analyze large volumes of structured data. Some specific scenarios where Amazon Redshift may be well-suited include: Data Warehousing, Business Intelligence, Data Migration as well as Real-Time Data Processing. On the other hand, Amazon Redshift may not be the most appropriate for unstructured data, organizations with low volume of data or Real-Time Stream Processing.
Cloudwalker offers analytic services for the gambling industry. The gambling industry has vast amounts of data that are high speed and variability. Our services from Redshift help gambling companies have better control of their bookmaking product, have a complete view of customers betting history, helps with detecting problematic accounts, etc.
Pros
Redshift has concurrency scaling helps serve more customers queries
Redshift has automatic table compression having less disc space consumed comparing to other data warehouse solutions
With ra3 new node types we can separate storage and compute
Having automatic vacuum delete helps having conzisent performance in cases where data variability in dwh production zone is present
Consistent service improvements from AWS: temporary tables, null handling in joins, single row inserts, materialized views
Cons
Frequent changes of management console look and feel
Automatic vacuum sort doesn't work for several billion rows tables
Disc IOPS performance monitoring excluded
Likelihood to Recommend
Redshift is great data warehouse solution if you have several billion rows tables. More than 200 very important improvements were added in several years' time. With new Redshift instance types solution has separation of storage and compute and magnitude better query response times. Don't use Redshift if you have less than several billion rows tables.
We have decided to purchase Amazon Redshift since we started the project of building a new "data lake," so the first step was to decide which tool would be more appropriate to use as a data warehouse. Since we have everything on the cloud, we choose Amazon Redshift to connect our current tools on AWS and integrate the data.
Pros
Data integration is very simple to perform
The tool provides some advice that is very useful
Their support is always complete and easygoing
Cons
Their documentation could be even better
Likelihood to Recommend
If you are looking for something easy to implement that will give you a nice performance, I would suggest Amazon Redshift. I'm using it in AWS environment, so I don't know if in another cloud environment the performance and all the features would be nice as well. It's also important to check if the price fits to you too.
VU
Verified User
Employee in Information Technology (11-50 employees)
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.
Pros
Complex queries
Aggregation
Fully managed service
Works very well with most BI/reporting solutions
Cons
Stored procedures
Job scheduling
A easier way (perhaps a GUI) to manage users permission
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
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.
Pros
Very fast, parallelized data loading from S3
Full ANSI SQL support
Highly scalable
Columnar storage
Cons
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
Likelihood to Recommend
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.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
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.
VU
Verified User
Consultant in Information Technology (11-50 employees)
