Transform the Way You Build Your Modern-Day Data Analytics Platform with AWS
by Neha Kaul, Managing Consultant – Altis Sydney
In this webinar, we discussed how you can transform the way you build your modern-day Data Analytics Platform using the Altis Automated Serverless Data Lake Framework.
This webinar was attended by a large group who came from varying industries and roles. With a growing emphasis on delivering data in AWS “Cloud” for analytics, there has been a big focus on automating this process and delivering a platform that is scalable, efficient and cost-effective. Use this link to view the free webinar, or watch it below.
We encourage you to connect with us and our team to talk about how we can help with your Modern Data Platform Journey, we can help you determine the readiness of your organisation and organise a more in-depth technical demonstration.
As a special offer Altis is offering:
- Free AWS data architecture workshop to discuss current and future state of your data platform
- A POC implementation of the AWS Data Lake Framework to start automatically ingesting data in days, possible production deployment afterwards.
- Current state review, future state definition and roadmap which covers the architecture, data governance and DataOps adoption
Below is a small highlight from our webinar –
There has been a growing need over the recent years to automate manual development processes for data consumption, integration and discovery. Enabling users to spend more time on innovation and deriving insights from the data than on repeatable processes not only reduces the overall project delivery cycle time but also increases productivity. The benefits of automating the iterative lifecycle of data architecture and enabling reusability of data ingestion pipelines also help businesses adopt a continuous and agile process to project delivery.
Benefits of automation can also extend into the management of the infrastructure of resources that are required for development processes. Focusing on automating this repeatable and manual process can allow your data team to focus on building the data platform, to gather the key business insights and enable you to make those critical business decisions rather than spending time on manually maintaining the infrastructure and resources they require.
‘Infrastructure as code’ now allows users to treat infrastructure simply as code where they can specify exactly the resources they need for their environment, save this code as a template that can be stored in a source control system and re-used anytime to spin up a new environment altogether. Automating this process not only provides greater agility and scalability but also cost efficiency for your team.
Leveraging the benefits of all these automated models and bringing together the extensive experience Altis has had in delivering several Data Analytics platforms, we have developed a framework that fully automates the process of extracting, transforming and loading the data into a central repository while reducing your cost of operations and the time-to-value, allowing your end-users to focus on the important task of data analysis and reporting.
Our key goal for building this framework was to provide you with the performance, flexibility and control to support the various rapid changes that your business goes through on a daily basis as well as help you grow this platform efficiently along with your business. To enable this, we ensured this solution is –
- Easily Scalable when it comes to compute and storage
- Fully managed ETL service
- Faster and cost-efficient queries
- Simple interactive querying interface for end-users
AWS Serverless Data Platform Framework
The following key components help perform the various functions of a modern data platform
- Data Ingestion: This involves collecting and ingesting the raw data from multiple sources
- Data Storage: Provides a secure, scalable and reliable data store. This data store can be used to store both the raw as well as processed data.
- Data catalog (metadata service): This service creates a catalog of the data that is ingested using its metadata including schema and location of the data.
- Data transformation: In this stage, the data is transformed from its raw state into a format that can be consumed by downstream services.
- Data analysis & Querying: Once the data has been transformed, it can be used by various business intelligence tools or in a self-service manner by users to derive insights from the data.
The diagram below illustrates the data flow and the services that make up the Altis AWS Serverless Data Lake Framework