IBM offers Db2 Big SQL, an enterprise grade hybrid ANSI-compliant SQL on Hadoop engine, delivering massively parallel processing (MPP) and advanced data query. Big SQL offers a single database connection or query for disparate sources such as HDFS, RDMS, NoSQL databases, object stores and WebHDFS.
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Amazon Redshift
Score 8.4 out of 10
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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
My recommendation obviously would depend on the application. But I think given the right requirements, IBM DB2 Big SQL is definitely a contender for a database platform. Especially when disparate data and multiple data stores are involved. I like the fact I can use the product to federate my data and make it look like it's all in one place. The engine is high performance and if you desire to use Hadoop, this could be your platform.
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)
[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.
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.
IBM DB2 is a solid service but hasn't seen much innovation over the past decade. It gets the job done and supports our IT operations across digital so it is fair.
Just very happy with the product, it fits our needs perfectly. Amazon pioneered the cloud and we have had a positive experience using RedShift. Really cool to be able to see your data housed and to be able to query and perform administrative tasks with ease.
IBM did a good job of supporting us during our evaluation and proof of concept. They were able to provide all necessary guidance, answer questions, help us architect it, etc. We were pleased with the support provided by the vendor. I will caveat and say this support was all before the sale, however, we have a ton of IBM products and they provide the same high level of support for all of them. I didn't see this being any different. I give IBM support two thumbs up!
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
MS SQL Server was ruled out given we didn't feel we could collapse environments. We thought of MS-SQL as more of a one for one replacement for Sybase ASE, i.e., server for server. SAP HANA was evaluated and given a big thumbs up but was rejected because the SQL would have to be rewritten at the time (now they have an accelerator so you don't have to). Also, there was a very low adoption rate within the enterprise. IBM DB2 Big SQL was not selected even though technically it achieved high scores, because we could not find readily available talent and low adoption rate within the enterprise (basically no adoption at the time). We ended up selecting Exadata because of the high adoption rate within the enterprise even though technically HANA and Big SQL were superior in our evaluations.
Than Vertica: Redshift is cheaper and AWS integrated (which was a plus because the whole company was on AWS). Than BigQuery: Redshift has a standard SQL interface, though recently I heard good things about BigQuery and would try it out again. Than Hive: Hive is great if you are in the PB+ range, but latencies tend to be much slower than Redshift and it is not suited for ad-hoc applications.
Redshift is relatively cheaper tool but since the pricing is dynamic, there is always a risk of exceeding the cost. Since most of our team is using it as self serve and there is no continuous tracking by a dedicated team, it really needs time & effort on analyst's side to know how much it is going to cost.
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.
