Data Warehousing is used to extract data in periodic stages, or as they are generated, making it more efficient and simpler to process queries over data that actually came from different sources. The raw data is turned into high-quality information to meet all enterprise reporting requirements and also for all levels of users.

Amazon Redshift is a data warehouse product that is fully managed, reliable, fast and a part of Amazon’s cloud computing platform, Amazon Web Services. Their product is built on the data warehouse technology MPP (Massive Parallel Processing) ParAccel by Actian. The product is a simple and cost-effective way to analyze all your business data using your existing business intelligence tools.

There were several traditional data warehousing techniques available which were used before the modern techniques became available. These techniques were developed to support the high-performance demands of any organization’s data. The types of applications that are supported include OLAP (Online Analytical Processing), Data Mining and DSS (Decision Support System). Let’s see how Amazon Redshift compares to traditional data warehousing techniques.

Comparing Amazon Redshift to Traditional Data Warehouses

Traditional data warehousing techniques are designed to support programmed functionalities such as:

  1. Roll-up: Data is generalized by summarizing it
  2. Pivot: Cross tabulation (rotation) is performed
  3. Slice and Dice: Performing projection operations on the dimensions
  4. Drill-down: Revealing more details
  5. Selection: Information is available by value and range
  6. Sorting: Data is sorted by ordinal value

The core benefits of data warehousing are as follows:

  • A collection of information for competitive and comparative analysis.
  • High-quality level of information enhancing completeness.
  • Disaster recovery plans with any other data backup source.

Amazing Redshift uses columnar storage technology in order to improve I/O efficiency and parallelized queries across multiple nodes and provide fast query performance. The service also offers custom ODBC and JDBC drivers which a developer can easily download it from the Connect Client tab of Console. It allows you to access the wide range of familiar SQL clients.

Optimized for Data Warehousing

Amazon Redshift uses efficient techniques and a variety of innovations in order to obtain a very high level of query performance on large amounts of datasets, ranging from hundred gigabytes to a petabyte or more. This is not possible in any traditional data warehousing technique to process an optimized query with this much data. Redshift has an MPP (Massively Parallel Processing) architecture, distributing SQL operations and parallelizing techniques to take full advantage of all available resources.


The Amazon Redshift can be easily scaled in just a few clicks through the AWS Management Console or by a simple API call. If your organization requires a change, you can easily add or remove a number of nodes in your cloud data warehouse. The scaling property in traditional data warehousing is not so easy and is very complex if you want to change your data warehousing structure. DS (Dense Storage) nodes allow you to handle very large data warehouse structure using HDDs (Hard Disk Drives).

Fully Managed

Monitoring, scaling and managing a traditional data warehouse can be challenging compared to Amazon Redshift. Automatic data backups, upgrades, and patches are services provided by Redshift that helps you focus on what’s important.  Your data and business.

Get started in minutes

Using simple API calls or the AWS Management Console, you can create a cluster, define its size, security profile and underlying node type. The complete data warehouse for your organization will be up and running in no time.  Data warehouses are important tools to use in order to access business insight and analysis into your business operations. There are some traditional ways available that can be used for data warehousing for any organization. These techniques can be challenging to implement, manage and analyze. Amazon Redshift provides an excellent solution for your data warehousing needs to create your cloud cluster, manage, scale and accessing insights.

Transfer your data into Redshift

Setting up your pipeline to load your data into Redshift smoothly and easily can be quite a project, costing your organization valuable time and resources. This is especially true if you want your data to be replicated at near real-time, which is usually the case for tracking important business metrics. This is where FlyData comes in. FlyData provides continuous, near real-time replication into Redshift from your transactional databases, such as MySQL, PostgreSQL, Amazon Aurora, and more. With an easy, one-time setup, our robust system ensures 100% accuracy with each load. Your data is always up to date.

For questions about FlyData and a no-pressure, risk-free assessment on how we can possibly help make your journey on to Amazon Redshift smoother and easier, connect with us at

FlyData handles real-time replication for Amazon RDS and Aurora, MySQL and PostgreSQL.

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