Best Data Engineering Course Training in Australia
- 40 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Complementary Hadoop and Spark
- Complementary ML on Cloud
- Complementary Python Programming
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Data engineering is about generating quality data and making it available for businesses to make data-driven decisions. Requirement for Data Engineering professionals has always outstripped the supply since 2017. Data Engineers enable businesses to engage in insights produced by data science using advanced analytics. This course in Data Engineering will equip you to build big data superhighways by teaching you the skills to unlock the value of data. According to reports, Data Engineer is the fastest-growing job in the space of technology, and with this course in Data Engineering, you will be able to kick start your new career as a Data Engineer today!
Data Engineering
Total Duration
3 Months
Prerequisites
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
Data Engineering Training Overview in Australia
With our Data Engineering Training, you get to explore the various tools used by Data Engineers and understand the difference between a Data Scientist and a Data Engineer. In this training, get introduced to tools like Python, Spark, Kafka, Jupyter. Spyder, TensorFlow, Keras, PyTorch, etc. along with advanced SQL techniques. Learn to extract raw data from various data sources in multiple formats and then transform them into actionable insights, and deploy them into a single, easy-to-query database. Learn how to build pipelines while handling huge data to optimize the process of big data. Get firsthand experience with advanced data engineering projects.
What is Data Engineering?
A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He has to pay attention to security and compliance; reliability and fidelity; scalability and efficiency; and flexibility and portability while designing, operationalizing and monitoring data processing systems.
Data Engineering Training Learning Outcomes in Australia
These modules will lay out the foundation for data science and analytics. The core of Data Engineering involves an understanding of various techniques like data modelling, building data engineering pipelines, and deploying the analytics models. Students will learn how to wrangle data and perform advance analytics to get the most value out of data. As you progress, you'll learn how to design as well as build data pipelines and work with big data of diverse complexity and production databases. You will also learn to extract and gather data from multiple sources, build data processing systems, optimize processes for big data, build data pipelines, and much more. With this course develop skills to use multiple data sources in a scalable way and also master the skills involved in descriptive and inferential statistics, interactive data analysis, regression analysis, forecasting, and hypothesis testing. Also, learn to
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Who Should Sign Up?
- Science, Math and Commerce graduates
- IT professionals who want to specialise in digital tech
- Professionals who want to move into Data Analytics
- Professionals who want to add Data Analytics to current job skills
- Academicians and researchers working in Data Analytics
Data Engineering Course Modules in Australia
These modules on Data Engineering are designed to ensure that they are at par with the current industry requirements for Data Engineers. All the modules will wrap up with hands-on practice using real tools and real-time multiple databases. With these modules you will learn to manage, load, extract, and transform data to facilitate delivering of results that your organization can leverage. You will also learn to master the core skills of cleansing, and migrating data.
- Intro to Data Engineering
- Data Science vs Data Engineering
- Building Data Engineering Infrastructure
- Working with Databases and various File formats (Data Lakes)
- SQL
- MySQL
- PostgreSQL
- NoSQL
- MongoDB
- HBase
- Apache Cassandra
- Cloud Sources
- Microsoft Azure SQL Database
- Amazon Relational Database Service
- Google Cloud SQL
- IBM Db2 on Cloud
- SQL
- Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
- Preprocessing, Cleaning, and Transforming Data
- Cloud Data Warehouse Service
- AWS: Amazon Redshift
- GCP: Google Big Query
- IBM: Db2 Warehouse
- Microsoft: Azure SQL Data Warehouse
- Distributed vs. Single Machine Environments
- Distributed Framework - Hadoop
- Various Tools in Distributed Framework to handle BigData
- HBase
- Kafka
- Spark
- Apache NiFi
- Distributed Computing on Cloud
- ML and AI platforms on Cloud
- Various Tools in Distributed Framework to handle BigData
- Databases and Pipelines
- Data Pipeline
- Features of Pipelines
- Building a pipeline using NiFi
- Data Pipeline
- Installing and Configuring the NiFi Registry
- Using the Registry in NiFi
- Versioning pipelines
- Monitoring pipelines
- Monitoring NiFi using GUI
- Using Pything with the NiFi REST API
- Building pipelines in Apache Airflow
- Airflow boilerplate
- Run the DAG
- Run the data pipelines
- Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Creating Databases
- Data Lakes
- Populating a data lake
- Reading and Scanning the data lake
- Insert and Query a staging database
- Building a Kafka Cluster
- Setup Zookeeper and Kafka Cluster
- Configuring and Testing Kafka Cluster
- Streaming Data with Apache Kafka
- Data Processing with Apache Spark
- Real-Time Edge Data with MiNiFi, Kafka, and Spark
Trends in Data Engineering Certification in Australia
A Data Engineer will analyze the given data and discover trends in the data sets and develop algorithms to sort the data to make it useful to the organization. To understand the organization's large data, Data Engineers need technical skills with good communication skills to work across various departments. 2021 saw a tremendous increase in usage of AI, MI, and Data Science in the industry. Data Engineering trends can be classified into Data Infrastructure, Data Architecture, and Data Management categories. While metadata management tools like data lineage, data quality, and data discovery will merge into the mainstream data management platform. To manage this platform Data Mesh Principles and change in data engineering structure should be made and serverless architecture is one among them. Cloud data warehouses will be the future of data management systems. A Data Engineer will be able to manage the main role in this process to develop, expand and deploy new technologies. Join our Data Engineering training program to grab the best opportunity in the growing job market.
How we prepare you
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Additional assignments of over 80+ hours
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Live Free Webinars
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Resume and LinkedIn Review Sessions
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Lifetime LMS Access
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24/7 support
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Job placements in Data Engineering fields
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Complimentary Courses
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Unlimited Mock Interview and Quiz Session
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Hands-on experience in a live project
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Offline Hiring Events
Call us Today!
+60 19-383 1378