“Customers expect a personalized buying experience across all the touchpoints they have with a company and a supply chain that is connected to the front office. They expect individualized offerings and next-day delivery, they expect transparency across the entire product lifecycle, they expect new payment models like pay as you go or subscription – and all this is impossible without true business process integration across the whole value chain.” This statement from Forbes truly highlights the importance of bringing information together in a meaningful manner to drive business strategy. Here we’ll discuss how companies can consolidate their valuable information assets by using data integration for Amazon Redshift Data Warehouse services.
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Table of Contents
- Data Integration for Amazon Redshift Overview
- Use Cases for Data Integration for Amazon Redshift
- Making The Case for Data Integration for Amazon Redshift
- How to Perform Data Integration for Amazon Redshift
- Limitations of Data Integration for Amazon Redshift
- How Integrate.io Can Help
Data Integration for Amazon Redshift Overview
Amazon Redshift is a relational database that offers a cost-effective cloud data warehouse managed service capable of processing structured and unstructured information. It enables companies to consolidate data from on-premises and cloud applications.
Redshift isn’t your typical warehouse, however. What makes it different from other providers is its performance when dealing with large volumes of information. An Amazon Redshift clusters and nodes capable of big data processing and querying exabytes of data faster than most other warehouse solutions.
Redshift is an OLAP (Online Analytical Processing, column-oriented database based on PostgreSQL version 8.0.2. which means it can support standard SQL queries. Like all other Amazon web services, Redshift features an online tool that lets customers manage their warehouse via a user-friendly dashboard.
Use Cases for Data Integration for Amazon Redshift
Redshift’s capability in processing large volumes of information makes it ideal for a wide variety of use cases. Due to the platform’s cost-effective pricing, companies of any size can take advantage of these features to meet their business needs.
In the age of digital transformation, agility and speed to market are vital to a company’s success. The ability to analyze information in real-time is a differentiator between companies that remain competitive and those that don’t. Many companies have information siloed throughout the organization and often spread throughout multiple systems. This disjointed view of the information makes it hard for companies to gain real insight into the inner workings of the company. It is when leaders can view information from a variety of data sources is where the true benefit lies. For example, companies can consolidate customer information, sales, support, marketing, and accounting information to gain a 360-view of their customer. This comprehensive view is what gives leaders the real insight needed to make informed strategic decisions.
It's not just leaders who rely on the vast amount of data the company has available. With the rise of the citizen integrator, business users need access to the information to build robust reports and dashboards.
Customer behavior analysis can help leaders understand how customers interact with their brands. This information is helpful for things such as planning for new features, identifying areas for improvement in the customer workflow identifying areas to enhance the customer experience in dealing with the company. That said, much of customer information data is stored in log files generated by each system with which the customer interacts. The warehouse can ingest this data and make it available for analysis.
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Making The Case for Data Integration for Amazon Redshift
Aside from the many use cases for which the platform can be used, Redshift has many additional features that make it an ideal storage solution.
The platform offers best-in-class performance with an ability to run up to 10 times faster than other data warehousing solutions. The tool uses Massively Parallel Processing (MPP) which essentially is a large number of resources working in parallel to perform the required queries.
Redshift uses machine learning (ML) to optimize query processing. With ML, the warehouse uses sophisticated algorithms to predict and classify incoming queries to plan workloads for the fastest performance. ML helps manage prioritization, concurrency, queuing, and dynamic memory allocation to achieve high performance.
Rather than running repeat queries which can cause latency issues. The platform caches the results of each query. Rather than repeating a query when it is requested, Redshift first checks the cache to see if there are results already available. If so, those results are presented to the user. This cuts down on query processing time and allows users to get their results faster.
The warehouse offers the scalability needed to run limitless concurrent queries. The tool can scale up to petabytes of information. What’s more, because it is a cloud platform, scalability is on-demand. That way, system performance won’t suffer while waiting on new resources to be provisioned. When the workload increases, Redshift automatically scales by adding additional cluster capacity.
As a cloud-native solution, all security configuration for the hardware is handled by Amazon. The company provides a variety of tools for companies to secure their data. These tools include:
- User Provisioning
- Virtual Private Cloud (VPC)
Companies that already leverage AWS services will appreciate the ease at which Redshift integrates with the other cloud services. By combining Redshift with these other services, companies can build a robust infrastructure entirely in the cloud.
How to Perform Data Integration for Amazon Redshift
The platform supports standard Data Manipulation Language (DML) commands, the COPY command, ETL, and AWS Database Migration Service. It also supports Amazon Athen and AWS Glue to load data from a wide variety of sources including:
- Amazon S3
- Amazon EMR Cluster
- Amazon RDS
- Amazon DynamoDB
- Microsoft SQL Server
- Cloud Applications
- CSV files
- JSON files
Redshift can also perform queries on S3 objects using Redshift Spectrum
As mentioned earlier, the ability to consolidate information from a variety of sources is how companies get the most value from the tool. Companies can use ETL to build pipelines from each of these sources to load data to the warehouse. Integrate.io’s no-code/low-code tool is an easy way for anyone, regardless of technical ability to build a pipeline.
Limitations of Data Integration for Amazon Redshift
Despite its many benefits, there are a few drawbacks to consider when evaluating the tool. These limitations include:
Constraints Aren’t Enforced
One of the basic tenets of a database is to ensure the uniqueness of information. However, Redshift does not provide such capability. While the incoming information may have primary/foreign keys defined, the platform does not enforce them. The reason for this is to maximize speed for performance.
Indexing in the system is handled by distribution and sort keys which is a new concept for many. This approach requests in-depth knowledge to ensure that it is configured properly to avoid performance issues.
Limited Parallel Uploads
MPP uploads are only supported for Amazon S3, Dynamo DB, and Amazon EMR. Anything else will require JDBC drivers, custom scripts ETL or ELT to load information.
Not Suitable as a Live Database
Although it has significant performance benefits, Redshift isn’t suited for serving information for live applications such as web apps. The tool is built primarily for reporting and analytics.
How Integrate.io Can Help
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Integrate.io is an all-in-one data integration platform that helps aggregate your datasets with Amazon Redshift and hundreds of other platforms. As a low-code development platform, Integrate.io is an AWS partner that provides technology to help companies quickly respond to market changes. The tool features a user-friendly graphical user interface. Users build pipelines by connecting a series of prebuilt connectors. Developers can take use API access to the platform to build pipelines.
Regardless of skill level, anyone can build a robust data pipeline in Xpleny to automate data aggregation. Reach out to us today to get set up for a free seven-day trial.