Lower your costs of running Data Warehouse using Amazon Redshift

Published by Nag Akula on

October 08, 2020
Nag Akula - Solution Architect
Lower your costs of running Data Warehouse using Amazon Redshift

Data Warehousing with Amazon Redshift

We live in a data-fueled business environment. Questions like, what is my customer’s next purchase going to be? Or where are my next customers going to come from? Etc. are all that can be answered using analysis of data. With the importance of data increasing, it has become critical that we ensure that it’s available when and where needed. It is also essential that the data is available in an easily consumable format. It is also vital that the business-as-usual activities do not get impacted due to various massive and complex data retrieval requests that usually comes from data scientists looking for new trends and insights. Today with data warehouse services like Redshift, it’s not uncommon for enterprises to operate multiple data warehouses and provision data warehouses on-demand.

Data needs to be moved and housed on a fast and effective platform that can help the business make decisions in a comfortable and fluid manner. Amazon’s Redshift is one such data warehouse service that is available on AWS (Amazon Web Services) cloud. It can quickly write large sums of data and enables fast retrievals.

Data warehouses are expensive to maintain though they are the backbone of any business application or operation. Realtime access to business-critical information makes data warehouses unique and costly. The higher performance of data warehouses demands higher costs. Even the price of having cloud warehouses can spike up based on the kind of workload that is run. The more demanding the analytics and models higher is the cost. This also makes cost quite unpredictable. It can be estimated that around 70-80% savings can be made just by optimizing the period of access to data warehouse services.

Redshift has come up with a new revolutionary feature to help customers save costs. The whole cluster can be frozen using a “Pause” functionality and can be started again using a “Resume” functionality. As long as it is paused the billing stops. Only the cost of backup storage needs to be paid. The Pause and Resume functions can be invoked through the Redshift console, through the AWS CLI (Command Line Interface) or scheduling can be done to Pause or Resume.

Source: https://aws.amazon.com/blogs/big-data/lower-your-costs-with-the-new-pause-and-resume-actions-on-amazon-redshift/

This feature is specifically useful when Redshift is required at regular intervals and for a short period. In development or a testing scenario this feature comes in very handy while running ETL (Extract Transform Load) activities. Redshift has access to any tool available on AWS. If you have an on-premise Data warehouse-like MS SQL Server or Oracle, AWS SCT Agent can migrate data to AWS S3 without many interventions. When moving terabytes of data, it is essential that the network performance does not get affected, and Data Processing is fast. AWS’s Snowball Edge service uses many AMS appliances that help to transfer data at high speeds. AMS Snowball can handle up to 80 terabytes of data and is secured through 256-bit encryption.

Redshift Benefits


The workloads can be replicated very quickly, so we do not have to think about reliability. The cost is based on usage of space, so the initial prices of using Redshift is Low till you scale up your business. Multiple instances can be created just by a click of a button.

Low Costs:

The initial costs are meager, and it is easy to spin up databases. Also, setting up multiple instances are easily managed with just a click of a button. Organizations have the option to choose the type of pricing model they prefer: on-demand or reserved instances. Those companies which require fewer data warehousing requirements can always go with on-demand as it fits their budget. The on-demand option still provides the flexibility of scaling to the companies.

Massively Parallel Processing:

The Redshift system is equipped with Massively Parallel Processing also referred by its short form as MPP. MPP allows engaging different processors to work on various parts of the same program simultaneously making the system highly available and fast. The running workloads are backed up regularly which helps to tackle any issue.

Source: https://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html

Supported with SQL:

Amazon Redshift has been built to handle online analytic processing (OLAP) and many BI (business intelligence) applications. Usually these workloads are complex and need to be dealt with through highly customized queries especially in the case of large datasets. As Redshift addresses varied requirements simultaneously, it uses the dedicated data structures and query execution engine. Amazon Redshift is a PostgreSQL based solution that was seen as a drop-in replacement for several Postgres based databases (where schemas are available in Postgres parlance). Redshift serves as a single point truth. This helps in forming insights that aid in decision making, historical outlooks and forecasting across various organizational verticals such as Finance, Marketing, and Medical Research. It is also possible to deliver data extracts to 3rd parties or visualize data on demand.

Data Processing:

It’s relatively easy to work with. We can store large application datasets very easily. It’s a high-performance tool that is highly scalable and supports large datasets. Data management is relatively easy and quick if your application already functions under AWS. It is excellent for generating reports, complex queries, and analytics. Redshift uses beneficial data compression features. It also provides you safe, easy, and reliable backups. You get quick outputs even with enormous queries. It’s relatively easy to maintain, and you don’t need to worry about hardware failures.

Utilize AWS Big Data Analytics:

Being within the AMS framework it is easy to utilize AWS tools to quickly create an analytics application of your own starting from scratch. You can scale up any Hadoop cluster within just a few minutes. This would mean handling significant loads of data at low costs.

When should you consider Redshift

If you are having high volume data transactions on AWS or planning to migrate to AWS, Redshift has features for your consideration that can help you kick start in a very cost-effective way. The Pause and Resume functions enable you to save costs. Redshift integrates with all AWS products very well. The data management is very easy and quick. Redshift is fast with big datasets. It provides fast data analytics across multiple columns. It is very good with complex queries and reports meaningful results. It has very low latency that makes it a fast-performing tool.

So, how do you now move your enterprise to the AWS cloud without interrupting business operations? Watch how one of our customers implemented Amazon Redshift and has reaped its benefits. Redshift has made a difference through fast data ingestion and reporting capabilities and Cambridge Technologies can help you harness the advantages of running your cloud warehouse and various Big Data applications keeping a tab on costs. Learn More.

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