Amazon Redshift

Easy setup (if you are on AWS Cloud Environment, just few clicks)

Easy learn (Good documentation)

Speed

It could bring some more features like we do have in Snowflake (Mainly the UI)

Data warehousing

Business intelligence

Data insights

Cost can be prohibitive

User interface could be more intuitive

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

Ease of setting up ETL

Uploading data into Redshift via AWS

Querying is quick

Missing option to restrict duplicate records

Lacks complex data sets like udf

Does not offer UI based querying & visualisation option like Looker

[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 super quick due to its Massive parallel processing

Amazon Redshift is compatible with many visualization tools which helps visualize the insights

Amazon redshift has almost 0 downtime and allows for a massive store of data

Since Redshift is a part of a larger AWS ecosystem, connecting with other resources is never a problem

Amazon redshift could have more detailed documentation including practical examples

Amazon redshift still lacks some of the advanced concepts which are possible with MS-SQL and others

It should have a feature where users can visualize the data stored for a better understanding

Data integration is very simple to perform

The tool provides some advice that is very useful

Their support is always complete and easygoing

Their documentation could be even better

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.

Robust as compared to traditional database/data warehouse

Offers significant query speeds

Low cost of ownership

Provides MPP only for AWS-supported storages

Prerequisites for configuring tables are not easy

Not great for use with web apps

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

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

Frequent changes of management console look and feel

Automatic vacuum sort doesn't work for several billion rows tables

Disc IOPS performance monitoring excluded

Aggregation

Extracting data

postgres Based

Could be faster

Limited sql workbenches

Expensive when speeding up the processing

It is very powerful, can hold anything you have.

It is scalable. Small or big, it can help you.

It is very fast. Can spin up cluster in minutes.

As data warehouse, it does not support fast I/O.

Learning can take more than expected.

You are not managing your data 100%.

Robust

Great UI

Price

Implementation in non AWS server

Replication is excellent, we did not have to worry about reliability.

Their auto-scaling feature came to our rescue when it came to cost management.

It became expensive over time as the data increased over time.

It could not separate users from using the same infrastructure.

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.

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.

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.

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

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.

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.

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

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.

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.

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.