Some decades ago, businesses found it very difficult to query data out of their Relational Databases Transaction Systems. This ushered in the rise of Online Analytical Processing (OLAP) as a proprietary solution to address the flexibility and speed of the queries. Ever since then, OLAP has thrived to minimize the amount of on-the-fly processing needed while navigating the data. Some new standards have emerged with the advent of technology, but the data optimization methods of OLAP are fundamentally still the same. This article will help you build an AWS OLAP cube.
Back in time, complicated searches and queries were very slow and took a lot of memory to store. An effective AWS OLAP solution enables fast and intuitive access to centralized data for the purposes of analysis and reporting. This article will help you create a Cloud-based AWS OLAP cube and ETL architecture that produces faster results at cheaper prices.
Table of Contents
- What is Amazon Redshift?
- Introduction to AWS Managed Services (AMS)
- Data Analytics Pipeline with AMS
- Building an AWS OLAP Cube
What is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale, Data Warehouse service in the Cloud. Redshift stores data in clusters that can be accessed in parallel. This is why Redshift data can be accessed quickly and with much ease. Each node can be accessed independently by users and applications.
You can use Redshift with a wide variety of data sources and data analytics tools and it can be integrated with many existing SQL-based clients. It has a good architecture that makes it easy to integrate the platform with many business intelligence tools.
Introduction to AWS Managed Services (AMS)
AWS Managed Services (AMS) helps you operate your Amazon Web Services (AWS) infrastructure more efficiently and securely. AMS is designed for enterprise customers who want to start operating in the Cloud at scale but don’t have the skills to do so. AMS helps these customers to reduce their operational costs by providing a perspective-guided, secure, and scalable architecture with a defined business outcome.
With an ever-growing list of AWS services and a library of automation, AMS can augment and optimize your data and operational capabilities in AWS environments. AMS helps customers achieve operational excellence by augmenting their Cloud operations skills. AMS provides you with operational flexibility, enhanced security, and compliance, and lets you focus on transforming your business in the Cloud.
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Data Analytics Pipeline with AMS
Now that you’re familiar with AWS Managed Services, let’s discuss its architecture in brief.
The data from OLTP Database is transformed by AWS Glue DataBrew. AWS Glue DataBrew is a no-code Data Transformation service that makes it easy for Data Analysts and Data Scientists to clean and prepare it for further analysis. The Amazon S3 transformed data is then collected by AWS Glue Crawler. AWS Glue Crawler collects metadata from the transformed S3 data and catalogs it for analytics and visualization using Amazon Athena and QuickSight. After this, Amazon SageMaker is used to build, train, and deploy Machine Learning models.
This architecture focuses on swiftly providing deep insights from your data to your users. On top of that, you don’t need any coding skills for Data Transformation, Data Analytics, and Machine Learning. You can automate filtering anomalies, data conversions, value corrections, and other tasks.
Building an AWS OLAP Cube
You can modernize your analytics workflow with the help of AWS and reduce the time it takes to perform enterprise-scale analytics. Lets discuss how you can create an AWS OLAP cube leveraging AWS’s capabilities in Data Cataloging, Data Visualization, and Machine Learning.
- Connecting to On-premises Databases
- Automatic Data Discovery
- No-code Data Transformation
- No-code Cataloging
- Data Analytics
- Data Visualization
Connecting to On-premises Databases
As discussed in the previous section, AMS architecture starts with the Online Transaction Processing (OLTP) Database in your company’s Data Centre. To perform OLAP workloads, you need to connect the OLTP Database to AWS Glue DataBrew using a Java Database Connectivity (JDBC) connection. DataBrew supports Data Sources using JDBC connection for common Data Stores such as Microsoft SQL Server, MySQL, Oracle, and PostgreSQL.
Automatic Data Discovery
DataBrew aids in Data Preparation and runs Data Transformation jobs without you having to write a single line of code. Simply put, DataBrew automates the process of Data Discovery allowing you to identify data patterns and anomalies.
No-code Data Transformation
To run AWS OLAP workloads, you need to create and run jobs based on the transformation steps, often referred to as DataBrew recipes. The recipe results are outputted to an Amazon Simple Storage Service (Amazon S3) bucket. You can automate your transformation workflows by scheduling DataBrew job runs. You can define the schedule of the jobs by a valid CRON expression.
The OLAP catalog is a set of metadata that sits between the actual OLAP data stored and applications. To create an OLAP data catalog, you can use AWS Glue Crawlers to automatically categorize your data in order to determine the data format, schema, and associated properties.
You can perform analytics on your data within AMS, by referring to the metadata definitions in the data catalog as references to the actual data in Amazon S3 using Amazon Athena.
Athena supports standard Structured Query Language (SQL) to directly query the transformed data into Amazon S3. Athena is serverless, hence you need not worry about its infrastructure management. There is no infrastructure to be managed and users pay only for the queries they run.
You can further visualize your OLAP workloads with the help of visualization and Business Intelligence (BI) tools. Amazon QuickSight, a scalable, serverless, ML-powered BI service is often used by enterprises to visualize curated data. You can easily develop and share attractive and informative BI dashboards that include ML-powered insights using QuickSight.
You can leverage Amazon SageMaker to incorporate Machine Learning workloads to OLAP workloads. SageMaker is a fully-managed and inexpensive Machine Learning service used by enterprises to develop, train, and deploy ML models into a production-ready hosted environment.
OLAP is a solution to multi-dimensional analytical queries in computing. OLAP encompasses Business Intelligence, Relational Databases, Data Mining, and Data Visualization. This post took you through various aspects of building an effective AWS OLAP cube to produce faster results. You were taken through various AWS Services such as DataBrew, Athena, QuickSight, and SageMaker which streamlines the process of enterprise-scale analytics. However, in businesses, extracting complex data from AWS OLAP Databases can be a challenging task and this is where Hevo saves the day!visit our website to explore hevo
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Share your experience of working with AWS OLAP cube in the comments section below.