Creating a Modern Analytics Architecture

October 20, 2020

Optimize Data Storage and Deploy a Modern Analytics Pipeline

We hear from organizations all the time that they are looking to extract more value from their data but struggle to capture, store, and analyze all the data generated by today’s modern and digital businesses. Data is growing exponentially, coming from new sources, increasingly diverse, and needs to be securely accessed and analyzed by any number of applications and people in shorter and shorter periods of time.

As the amount of data accumulates, organizations have stored it in different silos, making it difficult to perform analytics. To make it easier, organizations want all of their data in a single repository, i.e., a data lake. They want the flexibility to analyze the data in a variety of ways, using a broad set of analytic engines to ensure their needs will be met for their present and future analytics use cases. They also need to go beyond insights, from operational reporting on historical data to being able to perform real-time analytics and machine learning in order to accurately predict future outcomes.

Data lakes in the cloud are becoming a mainstream strategy for many organizations, providing promises of greater flexibility in the way data is handled and made available to decision-makers.

In this Book, you will learn about:

  • The challenge with existing data infrastructures including handling the variety of semi-structured and unstructured data, limited support for modern analytics, and the complexity of big data systems
  • Optimal data storage, data lake and data warehouse
  • Creating an analytics pipeline, including collecting, processing, storing, analyzing and visualzing data, and predicting future outcomes
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