DataOps Redefined!!!

What is DataOps?

The “purpose” of DataOps is to accelerate the process of extracting value from data. It does so by controlling the flow of data from source to value.

DataOps (also known as modern data engineering) is the industrialization of rapid data delivery and improvement in the enterprise (just as DevOps is for the development of software).

DataOps is a collaborative data management discipline that focuses on end-to-end data management and the elimination of data silos.

The orchestration of people, processes, and technology to accelerate the quick delivery of high-quality data to data users. Built on software development frameworks such as Agile, DevOps, and Statistical Process Control, DataOps offers the following benefits:

• Decreases the cycle time in deploying analytical solutions
• Lowers data defects
• Reduces the time required to resolve data defects
• Minimizes data silos

Problem Statement

Organizations are under competitive, disruptive, and regulatory pressures. Leveraging data and AI at the speed of business is the biggest differentiator. However, 81% of organizations don’t understand their data provides little to no value. For those aiming to succeed in digital transformation and AI, DataOps is essential to get to business-ready data providing automated, curated, and trusted data pipeline between data providers and data consumers. That means a scalable, agile, and faster path to achieving business objectives. In this session, IBM presents DataOps methodology with demonstrations to help you maximize your people, process and technology to accelerate journey to AI and digital transformation.

About this Course

As the data landscape becomes more complex with many new data sources, and data spread across data centres, cloud storage and multiple types of data store, the challenge of governing and integrating data gets progressively harder. The question is what can we do about it? This session looks at how a data lake, data fabric and data catalog software can be used to connect to and discover data across a distributed data landscape and enable continuous, component based development of data analytics pipelines to produce trusted re-usable data assets. This is otherwise known as Enterprise DataOps.

This Course looks at how a data lake, data fabric and data catalog software can be used to connect to data across a distributed data landscape and enable continuous, component based development of data analytics pipelines.

Duration

• 60 Hours

Cost of this

• INR 69000 | $1000

Level of this training

• Intermediate

Agenda of DataOps Course

• Models & Architecture – DataOps Concept and Foundation
• Platform – Operating Systems – Centos/Ubuntu & VirtualBox & Vagrant
• Platform – Cloud – AWS
• Platform – Containers – Docker
• Planning and Designing – Jira & Confulence
• Programming Language – Python
• Source Code Versioning – Git using Github
• Database 1 – Mysql
• Database 2 – Postgresql
• Data Analystics Engine – Apache Spark
• Container Orchestration – Kubernetes & Helm Introduction
• Reporting – Grafana
• ETL Tools – Apache Kafka
• Bigdata – Apache Hadoop
• DataOps Integration – Jenkins
• Big Data Tools for Visualization – Microsoft PowerBI
• Big Data Tools for Visualization – Tableau