- Published on
Architecture Options for Building an Analytics Application on AWS
- Authors
- Name
- Cloud Tech
- @AboutCloudTech
Architecture Options for Building an Analytics Application on AWS
Architecture Options for Building an Analytics Application on AWS is a Series containing different articles that cover the key scenarios that are common in many analytics applications and how they influence the design and architecture of your analytics environment in AWS. These series present the assumptions made for each of these scenarios, the common drivers for the design, and a reference architecture for how these scenarios should be implemented. A Thread [] 👀👇
Introduction to Modern Data Architecture (formerly Lake House)
A lake house is a modern data architecture that integrates a data lake, a data warehouse, and other purpose-built data stores while enabling unified governance and seamless data movement. More Details here: 👉
Introduction to Data Mesh
people have been talking about the data-driven organization model for years, which consists of data producers and consumers. This model is similar to those used by some of early adopting consumers and has been described by "Zhamak Dehghani of Thoughtworks", who coined the term data mesh. More Details here: 👉
Introduction to Batch Data Processing
On AWS, analytic services, such as Amazon EMR , Amazon Redshift , Lake Formation Blueprints , and AWS Glue family services namely Glue ETL , Glue Workflows , AWS Glue DataBrew , AWS Glue Elastic Views allow you to run batch data processing jobs at scale for all batch data processing use cases and for various personas, such as data engineer, data analyst, and data scientists. More Details here: 👉
Introduction to Streaming Ingestion and Stream Processing
Processing real-time streaming data requires throughput scalability, reliability, high availability, and low latency to support a variety of applications and workloads.
More Details here: 👉
Introduction to Operational analytics
Operational analytics refers to inter-disciplinary techniques and methodologies that aim to improve day-to-day business performance in terms of increasing efficiency of internal business processes and improving customer experience and value.
More Details here: 👉
Introduction to Data visualization Giving the decision-makers the opportunity to explore and interpret information in an interactive visual environment helps democratize data and accelerates data-driven insights that are easy to understand and navigate. Building a BI and data visualization service in the cloud allows you to take advantage of capabilities such as scalability, availability, redundancy, and enterprise grade security. More Details here: 👉
That’s all about Architecture Options for Building an Analytics Application on AWS! ❤
Thank you for Reading and Please share your thoughts !🤓
And if you haven't yet, make sure to follow me on below handles:
👋 connect with me on LinkedIn 🤓 connect with me on Twitter 🐱💻 follow me on github ✍️ Do Checkout my blogs
Like, share and follow me 🚀 for more content.
👨💻 Join our Cloud Tech Slack Community 👋 Follow us on Linkedin / Twitter for latest news 💻 Take a Look at our Github Repos to know more about our projects ✍️ Our Website