Amazon Web Services Redshift – Data Warehouse in the Cloud


Amazon Web Services has made publicly available its fully managed, petabyte-scale data warehouse cloud service in February, 2013. It promises a high performance, secure, easily scalable data warehouse solution that costs 1/10th of a traditional data warehouse (less than 1,000 USD/TB/year, according to the AWS Introduction to Redshift presentation: , it is compatible with the traditional BI tools and ready to be running within minutes. As of writing this article the service is available in US East region only but supposed to be rolled out to other regions, too. The service is manageable via the regular AWS tools: AWS management console, command line tools (aws commands based on python) and API based on HTTP requests/responses.

Under the hood

Under the hood, AWS Redshift is based on PostgreSQL 8.0.2. The architecture consist of 1 leader node – a node which is responsible for managing the communications with the clients, developing the execution plan and then distributing the compiled code to the compute nodes-, and 1 or more compute nodes that are exetung the code and then sending back the result to the leader node for aggregation. The compute nodes can have either 2-cores, 15GB RAM and 2 TB storage node (dubbed as XL node) or a 16-cores, 120 GB RAM and 16 TB storage node (dubbed as 8XL node). More details about the Redshift archtecture can be found at


Launching a cluster

The easiest way to launch a cluster is via AWS console.

We need to define the basic attributes like cluster identifier, database name, database port, master username and password:


Then we need to select the node type (XL or 8XL) and the number of compute nodes. A cluster can be single or multi-node, the minimum config is a one XL node cluster, while the maximum config is sixteen 8XL nodes – you can do the math in terms of cores, memory and storage.


Then we can configure additional parameters (like database encyption or security groups)


We can then review the configuration and are ready to launch the service:


The status will be first “creating” for a while then it will become “available”. This is when the JDBC url will become known and can be used for configuring the clients.


In order to make the service accessible, we need to configure the security options (either a security group – if Redshift is going to be accessed from EC2 – or a CIDR/IP (Classless- Inter-Domain Routing IP range)  – if Redshift is to be accessed from public Internet.  The system will automatically recognise the IP address of the client connected to AWS console.


And that is it! From then on the client can be connected to the Redshift cluster.

We used SQLWorkbench to test the service, the same way as suggested by AWS Redshift documentation. It is a Java based open source SQL tool. The connection parameters are the standard JDBC attributes:


The PostgreSQL version can be checked using

select version();

PostgreSQL 8.0.2 on i686-pc-linux-gnu, compiled by GCC gcc (GCC) 3.4.2 20041017 (Red Hat 3.4.2-6.fc3), Redshift 1.0.546

We tested the service with Amazon stock prices downloaded from Yahoo Finance.

The content has been uploaded to a S3 bucket called stockprice (S3://stockprice/amzn.csv). We had to make it accessible for everyone to read. (open/download).

Then we could create the appropriate table using standard SQL command:

CREATE TABLE stockprice (
     stockdate date not null,
     stockopen decimal(6,2),
     stockhigh decimal(6,2),
     stocklow decimal(6,2),
     stockclose decimal(6,2),
     stockvolume integer,
     stockadjclose decimal(6,2)

Table 'stockprice' created

Execution time: 0.15s

desc stockprice
stockdate	date	NO	YES		NO		1
stockopen	numeric(6,2)	NO	YES		NO		2
stockhigh	numeric(6,2)	NO	YES		NO		3
stocklow	numeric(6,2)	NO	YES		NO		4
stockclose	numeric(6,2)	NO	YES		NO		5
stockvolume	integer	NO	YES		NO		6
stockadjclose	numeric(6,2)	NO	YES		NO		7

To load the data into stockprice table, we had to use copy command with the S3 source file (it could also be an Amazon DynamoDB source).

copy stockprice from 's3://stockprices/amzn.csv' CREDENTIALS 'aws_access_key_id=XXXXXXX;aws_secret_access_key=XXXXXXX' delimiter ',';

If there is any error during the load operation, it can be verified by running a select statement on the stl_load_errors table. (e.g. incorrect data format).

And then we can run our SQL statements to analyse the data.

select * from stockprice order by stockadjclose desc limit 100;

stockdate	stockopen	stockhigh	stocklow	stockclose	stockvolume	stockadjclose
2013-01-25	275.00	284.72	274.40	283.99	4968100	283.99
2013-01-28	283.78	284.48	274.40	276.04	4321400	276.04
2013-03-05	274.00	276.68	269.99	275.59	3686200	275.59
2013-03-13	275.24	276.50	272.64	275.10	1884200	275.10
2013-03-08	275.00	275.44	271.50	274.19	1879800	274.19
2013-03-12	271.00	277.40	270.36	274.13	3246200	274.13
2013-03-07	274.10	274.80	271.85	273.88	1939000	273.88
2013-03-06	275.76	276.49	271.83	273.79	2050700	273.79
2013-01-24	269.37	276.65	269.37	273.46	3417000	273.46
2013-03-04	265.36	273.30	264.14	273.11	3453000	273.11
2013-01-30	283.00	284.20	267.11	272.76	13075400	272.76
2013-01-14	268.00	274.26	267.54	272.73	4275000	272.73
2013-01-18	270.83	274.50	269.60	272.12	2942000	272.12
2013-01-15	270.68	272.73	269.30	271.90	2326900	271.90
2013-03-11	273.43	273.99	270.40	271.24	1904900	271.24

AWS console supports various management functions of the cluster, we can reboot the cluster, we can modify parameters, we can resize it by defining different node type (XL->8XL) or decreasing/increasing the number of nodes. We can also delete the cluster via AWS console.


Amazon Web Services Redshift is another big step to make cloud services available for enterprise computing. It offers a data warehouse capability with minimal effort to start up and scale as operations demand. It is a great complement to other database services such as DynamoDB for NoSQL requirements and RDS for relational database services.