DB2 is a family of relational database software solutions offered by IBM. It includes standard Db2 and Db2 Warehouse editions, either deployable on-cloud, or on-premise.
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Amazon Redshift
Score 8.9 out of 10
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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
We are underway to evaluate both, in their benefits versus concerns. One thing that makes IBM Db2 better is it is a very mature database with great performance
The decision to select IBM Db2 is typically driven by specific enterprise needs:Mission-Critical Workloads: Db2 is built to handle highly demanding transactional (OLTP) and analytical (OLAP) workloads with high reliability and continuous availability via features like Db2 …
Db2 for i SQL is much more standard compliant than SQL Servers dialect. And with Visual Explain its much easier to optimize SQL queries for optimal performance.
I have used DBArtisan before in another project with similar use cases, and both are very reliable and work well. As this application is compatible with their platforms, the current company is using the IBM db2 for the work.
Oracle and Microsoft are the ones that we have more to compare with and they are on par with Db2. Postgres is the small solution that usually we leave behind and move to Db2. Mongo is the one that is different from what I used Db2 for but I know it has the capability to use …
Amazon Redshifts has fewer features but at the same time, you also have some gains once it is running on AWS Cloud and it is really easy to set up. Besides that, in our case, it is a bit cheaper and we don't really need the extra features that you can find on Snowflake. Another …
Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective …
Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user …
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a …
Redshift leapfrogged Hive back when Hive was trying to figure out how to implement indexes, providing a more stable, standardized (postgres), easy to use (any postgres client), easier to administer, and scalable solution for querying server logs and raw usage data.
Amazon Redshift is one of the fastest service offerings available in the market now. Plus you get an advantage of using a cutting edge compute service offering from AWS. Other technologies are fast but not as good as Amazon Redshift, I would say. Our business is interested in …
1. Redshift has better compression (automated) consuming less space then competitors 2. Automated Vacuum Delete for having consistent performance 3. AWS introduced ra3 node types for simple separation of compute and storage
Its definitely an improvement on all fronts for our business needs. Again, our MySQL server was really slow and we needed a more efficient solution. It was a major upgrade, but it is much more expensive than an in house server. It was expected but I'd say that lots of headaches …
Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend …
Amazon Redshift has a better UI, hands down. And it is easy to integrate with bigger tools like Talend. It has many issues when it comes to understanding the architect perspective like Toad, which has a better UI for architect data together. However, that is because we are not …
We like Snowflake for its separation of computing and storage and also the separation of data warehouse different users. We replaced Redshift with Snowflake. However, Snowflake is great for its pay for performance kind of methodology.
Azure SQL Database was discarded because of a less attractive licensing, costs, plus its integrates poorly with many of the Azure offerings as say Azure Data Factory - it is not a true ETL yet. Also, the rest of the tools used were of Open Source type and it did not look like a …
It works on the cloud and we use the platform Dbeaver which is very unique and easy to maintain. There are very limited tools of this kind but the security issues are pretty high within those tools.
As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. …
We are currently on Redshift, because it was out before Snowflake. However, Snowflake looks promising. It's the new shiny toy that gives options that Redshift does not provide for. The big thing is that storage and compute can be scaled separately, whereas you cannot do that in …
Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of …
Snowflake supports semi-structured data types and provided solutions to manage/process the semi-structured data. It supported sharing data between the different accounts and makes it easy in the scale and scale down process. Snowflake doesn't limit users on the database.
Amazon Redshift is much easier to set up and start using. It interacts well with the PostgreSQL client (psql) and shares certain basic data dictionary, and people familiar with PostgreSQL feel right at home. The cluster is part of AWS services offering, and it works well with …
Some organizations use PostgreSQL as an OLAP store. PostgreSQL offers a modern SQL dialect, data types, and features that Redshift lacks. RDS is a great managed PostgreSQL product. However, PostgreSQL is a poor choice for a data warehouse. It's row-oriented storage requires …
I have primarily used it as the basis for a SIS - but I have migrated more than a few systems from there database systems to DB2 (Filemaker, MySQL, etc.). DB2 does have a better structural approach, as opposed to Filemaker, which allows for more data consistency, but this can also lead to an inflexibility that can sometimes be counterintuitive when attempting to compensate for the flexibility of the work environment as Schools tend to have an all in one approach.
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)
DB2 maintains itself very well. The Task Scheduler component of DB2 allows for statistics gathering and reorganization of indexes and tables without user interaction or without specific knowledge of cron or Windows Task Scheduler / Scheduled jobs.
Its use of ASYNC, NEARSYNC, and SYNC HADR (High Availability Disaster Recovery ) models gives you a range of options for maintaining a very high uptime ratio. Failover from PRIMARY to SECONDARY becomes very easy with just a single command or windowed mouse click.
Task Scheduler ( DB2 9.7 and earlier ) allows for jobs to be run within other jobs, and exit and error codes can define what other jobs are run. This allows for ease of maintenance without third party softwares.
Tablespace usage and automatic storage help keep your data segmented while at rest, making partitioning easier.
Ability to run commands via CLI (Command Line Interface) or via Control Center / Data Studio ( DB2 10.x+) makes administration a breeze.
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.
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.
The DB2 database is a solid option for our school. We have been on this journey now for 3-4 years so we are still adapting to what it can do. We will renew our use of DB2 because we don’t see. Major need to change. Also, changing a main database in a school environment is a major project, so we’ll avoid that if possible.
You have to be well versed in using the technology, not only from a GUI interface but from a command line interface to successfully use this software to its fullest.
Overall it serves all our aspects of data management like data cleaning, data manipulation, and data reporting on the cloud platform. We can create stored procedures and triggers in it very easily as all the options are self suggested in it. We can easily attach the results of ARS to the other tools as well for drawing the statistical results.
I have never had DB2 go down unexpectedly. It just works solidly every day. When I look at the logs, sometimes DB2 has figured out there was a need to build an index. Instead of waiting for me to do it, the database automatically created the index for me. At my current company, we have had zero issues for the past 8 years. We have upgrade the server 3 times and upgraded the OS each time and the only thing we saw was that DB2 got better and faster. It is simply amazing.
The performances are exceptional if you take care to maintain the database. It is a very powerful tool and at the same time very easy to use. In our installation, we expect a DB machine on the mainframe with access to the database through ODBC connectors directly from branch servers, with fabulous end users experience.
Easily the best product support team. :) Whenever we have questions, they have answered those in a timely manner and we like how they go above and beyond to help.
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
With the other two mentioned above, I needed to have processes and frameworks that executed outside of the environment driving DB management operations. Yes, these are completely different solutions; however, the support you get for framework, library, and language support allows for runtime at a different layer than with other solutions.
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a data store. [Amazon] Redshift was ideal with the price structure, performance and ROI[.]
DB2 can be configured and can work with a variety of applications as opposed to how it was designed initially to only with with IBM mainframes. It's easy implementation process makes it a good buy for many organizations to scale their applications to be the best in terms of versatility, resilience and application performance