Hadoop vs Amazon Redshift

• HDFS is reliable and solid, and in my experience with it, there are very few problems using it
• Enterprise support from different vendors makes it easier to 'sell' inside an enterprise
• It provides High Scalability and Redundancy
• Horizontal scaling and distributed architecture

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• 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.

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• Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
• Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
• Hadoop cannot be used for running interactive jobs or analytics.

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• 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.

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• Hadoop has allowed us to scale out a few of our tier-1, customer facing applications to provide very fast access to reports and analytics.
• Hadoop was easily implemented by our Linux team and onboarded by our Hadoop Admins.
• Hadoop has been a very stable platform and only goes down due to server patching or other maintenance.

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• Redshift has had a very positive impact on our business. It has been used to provide analytics on marketing campaigns to boost revenue.
• Redshift is instrumental in our payment collection business processes. It powers everything from who gets called to who gets sent collection emails.

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