About DataOps Certification
This DevOpsSchool’s Certification Program in DataOps leverages the superiority of DevOpsSchool’s academic eminence. The program covers critical Data Science topics like Apache Spark, Grafana, Apache Hadoop, Microsoft PowerBI, Tableau, postgresql and Kubernetes and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
Our Certification is Associated with DevOpsSchool and we offer certification programs called as “DataOps Certified Professional (DOCP)”.
Demystify data and strengthen your analytical skills through this DataOps Program in Business Analytics, that delivers a high engagement learning experience with real-world applications to master business analytics.
After completing the training, those who pass a test will receive DataOps certification. The Certification Course Certifications “DataOps Certified Professional” can be accessed on the DevOpsSchool website at the following link.https://www.devopsschool.com/certificates/
Highlight of DataOps Certifications
- Introduction to DataOps
- DataOps Production Pipelines
- DataOps Development Pipelines
- DataOps Environment Pipelines
- DataOps Implementation with Tools
DataOps Certification Curriculum
DataOps Concept and Foundation
- The Problem with Datascience
- The knowledge Gap
- Lack of Support
- Challenges of of Data Analytics
Agile Collaboration for DataOps
- DataOps Manifesto
- DataOps Principles
- Data Science Life-Cycle
DevOps for DataOps
- Development and Operations
- Fast Flow from Continous Delivery
- Reproducible Environments
- Deployment Pipelines
- Continous Integration
- Automated Testing
Deployment and Release Processes
- Self-Service Deployments
- Release Processes
- DevOps Measurements
- Review Processes
- DevOps for Data Analytics
- The Data Conflict
- Data Pipeline Environments
- Data Pipeline Orchestration
- Data Pipeline Continous Integration
- Tools based on DataOps Values and Principles
- DataOps Technology Ecosystem
- The Assembly Line
- Data Integration
- Data Preparation
- Stream Processing
- Data Management
- Reproducibility, Deployment, Orchestration, and Monitoring
- Compute Infrastructure and Query Execution Engines
- Data Storage
- DataOps Platforms
- Data Analytics Tools
DataOps Tools Training
- 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
- Container Orchestration – Kubernetes & Helm Introduction
- Database – Mysql
- Database – postgresql
- Data Analystics Engine – Apache Spark
- 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
The Qualifications for a DataOps Engineer
Most DataOps engineers have a degree in computer science, and are fluent in multiple coding languages. DataOps engineers need to have a strong understanding of the different development approaches and they should have good people skills.
What does DataOps stand for?
DataOps (data operations) is an Agile approach to designing, implementing and maintaining a distributed data architecture that will support a wide range of open source tools and frameworks in production. The goal of DataOps is to create business value from big data.
What is the difference between DataOps and DevOps?
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DevOps is the transformation in the delivery capability of development and software teams whereas DataOps focuses much on the transforming intelligence systems and analytic models by data analysts and data engineers.
How do I start DataOps?
A practical guide to get started with DataOps
Conceptualize: generate the ideas and business cases for analytical data products. …
Experiment: prove the feasibility and value of an idea. …
Operationalize: move a proven idea to production and make the operational changes to leverage it.
What do DataOps engineer do?
A DataOps engineer helps an organization operationalize its data by creating the environment and processes needed to efficiently manage data and derive value from analytics.
Is DataOps same as data engineering?
DataOps engineer owns the data pipelines and the general workflow while data scientists and developers operate inside the pipelines. A DataOps engineer’s primary role is to help data engineers and analysts streamline the product development through implementing DevOps concepts towards the data pipeline.
Why is DataOps needed?
DataOps helps overcome the hurdles and complexities and deliver analytics with speed and agility, without compromising on data quality. It derives inspiration from the practices of Lean Manufacturing, Agile and DevOps.
Which pays more data science or DevOps?
Data scientist, which has the top ranking, offers a median salary of $110,000. DevOps engineer, which ranks second, also offers a median salary of $110,000, while data engineer in third place offers a median salary of $106,000. Analytics manager ranks fifth but offers a higher salary of $112,000.
What are DataOps principles?
DataOps takes inspiration from principles of Agile, DevOps, and Lean Manufacturing – and involves the same to have better management of data teams, processes and people – which is crucial – as being data-driven can be a significant moat for your business, in this decade and even next.