Updated the Redshift section.

More tips and gotchas.
Some corrections.

README.md

@@ -1140,7 +1140,7 @@ Redshift
 ### Redshift Basics
 
 -	📒 [Homepage](https://aws.amazon.com/redshift/) ∙ [Developer guide](http://docs.aws.amazon.com/redshift/latest/dg/) ∙ [FAQ](https://aws.amazon.com/redshift/faqs/) ∙ [Pricing](https://aws.amazon.com/redshift/pricing/)
--	**Redshift** is AWS’ [data warehouse](https://en.wikipedia.org/wiki/Data_warehouse) solution, which is highly parallel, share-nothing, and columnar. It is very widely used. It [was built](https://en.wikipedia.org/wiki/Amazon_Redshift) with [ParAccel](https://en.wikipedia.org/wiki/ParAccel) and [Postgres](https://en.wikipedia.org/wiki/PostgreSQL).
+-	**Redshift** is AWS’ managed [data warehouse](https://en.wikipedia.org/wiki/Data_warehouse) solution, which is massively parallel, scalable, and columnar. It is very widely used. It [was built](https://en.wikipedia.org/wiki/Amazon_Redshift) using [ParAccel](https://en.wikipedia.org/wiki/ParAccel) technology and exposes [Postgres](https://en.wikipedia.org/wiki/PostgreSQL)-compatible interfaces.
 
 ### Redshift Alternatives and Lock-in
 
@@ -1148,18 +1148,25 @@ Redshift
 
 ### Redshift Tips
 
--	🔸Although Redshift is based on Postgres, its SQL dialect and performance profile are different.
+-	Although Redshift is mostly Postgres-compatible, its SQL dialect and performance profile are different.
 -	Redshift supports only [11 primitive data types](https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html). ([List of unsupported Postgres types](https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-datatypes.html)\)
 -	It has a leader node and computation nodes (the leader node distributes queries to the computation ones). Note that some functions [can be executed only on the lead node.](https://docs.aws.amazon.com/redshift/latest/dg/c_SQL_functions_leader_node_only.html)
--	🔸Redshift does not support many Postgres functions, most notable date/time related or aggregates. See the [full list here](https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-functions.html).
 -	Major 3rd-party BI tools support Redshift integration (see [Quora](https://www.quora.com/Which-BI-visualisation-solution-goes-best-with-Redshift)).
+-	[Top 10 Performance Tuning Techniques for Amazon Redshift](https://blogs.aws.amazon.com/bigdata/post/Tx31034QG0G3ED1/Top-10-Performance-Tuning-Techniques-for-Amazon-Redshift) provides an excellent list of performance tuning techniques.
+-	[Amazon Redshift Utils](https://github.com/awslabs/amazon-redshift-utils) contains useful utilities, scripts and views to simplify Redshift ops.
 
 ### Redshift Gotchas and Limitations
 
--	🔸While Redshift can handle heavy queries well, it does not scale horizontally, i.e. does not handle multiple queries in parallel. Therefore, if you expect a high parallel load, consider replicating or (if possible) sharding your data across multiple clusters.
--	Redshift data commit transactions are very expensive and serialized at the cluster level. Therefore, consider grouping multiple COPY commands into a single transaction whenever possible.
--	🔸Redshift does not support multi-AZ deployments. Building multi-AZ clusters is not trivial. [Here ](https://blogs.aws.amazon.com/bigdata/post/Tx13ZDHZANSX9UX/Building-Multi-AZ-or-Multi-Region-Amazon-Redshift-Clusters)is an example using Kinesis.
--	🔸Redshift has reserved keywords which are not present in Postgres (see full list [here](https://docs.aws.amazon.com/redshift/latest/dg/r_pg_keywords.html)). Watch out for DELTA ([Delta Encodings](https://docs.aws.amazon.com/redshift/latest/dg/c_Delta_encoding.html)).
+-	While Redshift can handle heavy queries well, its does not scale horizontally, i.e. does not handle multiple queries in parallel. Therefore, if you expect a high parallel load, consider replicating or (if possible) sharding your data across multiple clusters.
+-	Leader node is the single point of failure.
+-	Redshift data commit transactions are very expensive and serialized at the cluster level. Therefore, consider grouping multiple mutation commands (COPY/INSERT/UPDATE) commands into a single transaction whenever possible.
+-	Redshift does not support multi-AZ deployments. Building multi-AZ clusters is not trivial. [Here](https://blogs.aws.amazon.com/bigdata/post/Tx13ZDHZANSX9UX/Building-Multi-AZ-or-Multi-Region-Amazon-Redshift-Clusters) is an example using Kinesis.
+-	Beware of storing multiple small tables in Redshift. The way Redshift tables are laid out on disk makes it impractical. The minimum space required to store a table (in MB) is nodes * slices/node * columns. For example, on a 16 node cluster an empty table with 20 columns will occupy 640MB on disk.
+-	Although most Redshift queries parallel well at the compute node level, certain stages are executed on the leader node, which becomes the bottleneck.
+-	Query performance degrades significatly during data ingestion. [WLM (Workload Management)](http://docs.aws.amazon.com/redshift/latest/dg/c_workload_mngmt_classification.html) tweaks help to some extent. However, if you need consistent read performance, consider having replica clusters (at the extra cost) and swap them during update.
+-	Never resize a live cluster. The resize operation takes 10-15 hours depending on the dataset size. In rare cases, the operation may also get stuck and you'll end up having the non-fuctional cluster. The safer approach is to create a new cluster from a snapshot, resize the new cluster and shutdown the old one.
+-	Redshift has reserved keywords which are not present in Postgres (see full list [here](https://docs.aws.amazon.com/redshift/latest/dg/r_pg_keywords.html)). Watch out for DELTA ([Delta Encodings](https://docs.aws.amazon.com/redshift/latest/dg/c_Delta_encoding.html)).
+-	Redshift does not support many Postgres functions, most notable date/time related or aggregates. See the [full list here](https://docs.aws.amazon.com/redshift/latest/dg/c_unsupported-postgresql-functions.html).
 
 EMR
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