Best Data Engineering Course Training in Kompally, Hyderabad
- 60 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 Training Overview
Certified Course on Data Engineering explores the various tools Data Engineers use and find out the difference in the job responsibilities of a Data Scientist and a Data Engineer. It expands learners' understanding of the various skills involved in knowing tools like Python, Spark, Kafka, and Jupyter. Spyder, TensorFlow, Keras, PyTorch, etc., with advanced SQL techniques. Students get a chance to extract raw data from various data sources in multiple formats and transform them into actionable insights, and deploy them into a single, easy-to-query database. They learn to handle huge data sets and build data pipelines to optimize processes for big data. Students get a chance to dive deeper into advanced data engineering projects that will help in gaining practical experience.
What is Data Engineering?
A Data Engineer collects and transforms data to empower businesses to make data-driven decisions. He/She 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 Kompally
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, Maths, Compute Graduates
- IT professionals who want to Specialize in Digital Tech
- SQL and related developers or software developers
- Students/IT professionals having an interest in Data and Databases
- Professionals working in the space of Data Analytics
- Academicians and Researchers working in the space of Data Analytics/Science
- Cloud and BigData enthusiasts
Data Engineering Course Modules in Kompally
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.
- Data Science vs Data Engineering
- Data Engineering Infrastructure and Data Pipeline
- Data Architecture
- Lambda
- Kappa
- Streaming Big Data Architectures Monitoring pipelines
- With Databases and various File formats (Data Lakes)
- SQL
- MySQL
- PostgreSQL
- NoSQL
- MongoDB
- HBase
- Amazon Relational Database Service
- Microsoft Azure SQL Database
- Google Cloud SQL
- Concepts of Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
- Getting started with Python programming for Data Processing
- Data Types
- Python Packages
- Loops and Conditional Statements
- Functions
- Collections
- String Handling
- File handling
- Exceptional Handling
- MySQL Integration
- INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations
- MongoDB Integration
- Pre-processing, Cleaning, and Transforming Data
- Linux OS
- Apache Hadoop
- HDFS
- Hadoop Cluster on GCP - Dataproc
- Spark Components
- Spark Executions – Spark Session
- RDD
- Spark DataFrames
- Spark Core
- Spark SQL
- Spark MLlibs
- Spark Streaming
- Big Data and Apache Kafka
- Producers and Consumers
- Clusters Architectures
- Kafka Streams
- Kafka pipeline transformations
- Building pipelines in Apache Airflow
- Deploy and Monitor Data Pipelines
- Production Data Pipeline
- Data Lake Cloud offerings
- Cloud Data Warehouse Services
- Introduction to AWS platform, Creation of free account
- Walk through the platform and services offered by AWS
- IAM - Identity and Access Management
- Intro to AWS Data Warehouses, Data Marts, Data Lakes, and ETL/ELT pipelines
- Configuring the AWS Command Line Interface tool
- Creating an S3 bucket
- Working with Databases and various File formats (Data Lakes)
- Amazon Database Migration Service (DMS) for ingesting data
- Amazon Kinesis and Amazon MSK for streaming data
- AWS Lambda for transforming data
- AWS Glue for orchestrating big data pipelines
- Consuming data - Amazon Redshift & Amazon Athena for SQL queries
- Introduction to Microsoft Azure platform, Creation of free account
- Walk through the platform and services offered by Azure
- IAM - Identity and Access Management
- Azure Data Lake - Managing Data
- Securing and Monitoring Data
- Introduction to Azure Data Factory (ADF)
- Building Data Ingestion Pipelines Using Azure Data Factory
- Azure Data Factory Integration Runtime
- Configuring Azure SQL Database
- Processing Data with Azure Databricks
- Introduction to Azure Synapse Analytics
- Data Transformations with Azure Synapse Dataflows
- Monitoring And Maintaining Azure Data Engineering Pipelines
- Introduction to GCP platform, Creation of free account
- Walk through the platform and services offered by GCP
- IAM - Identity and Access Management
- Bigdata Solutions with GCP Components
- Data Warehouse - BigQuery
- Processing ETL/ELT pipelines with Data Fusion
- Connecting BI tool for visualizing Data with Looker Studio
- Architecting Data Pipelines
- CI/CD On Google Cloud Platform for Data Engineers
- Storage Accounts
- Designing Data Storage Structures
- Data Partitioning
- Designing the Serving Layer
- Physical Data Storage Structures
- Logical Data Structures
- The Serving Layer
- Data Policies & Standards
- Securing Data Access
- Securing Data
- Data Lake Storage
- Data Flow Transformations
- Databricks
- Databrick Processing
- Stream Analytics
- Synapse Analytics
- Data Storage Monitoring
- Data Process Monitoring
- Data Solution Optimization
- Google Cloud Platform Fundamentals
- Google Cloud Platform Storage and Analytics
- Deeper through GCP Analytics and Scaling
- GCP Network Data Processing Models
- Google Cloud Dataproc
- Dataproc Architecture
- Continued Dataproc Operations
- Implementations with BigQuery for Big Data
- Fundamentals of Big Query
- APIs and Machine Learning
- Dataflow Autoscaling Pipelines
- Machine Learning with TensorFlow and Cloud ML
- GCP Engineering and Streaming Architecture
- Streaming Pipelines and Analytics
- GCP Big Data and Security
- Certificate Course in Data Engineering by SUNY
Trends in Data Engineering Certification in Kompally
Data engineering is fundamental to managing data and automating workflows. Data Engineers ensure that the data is analyzed to produce high-quality data that businesses can utilize to finding the latest trends. This year will see a tremendous elevation in the use of artificial intelligence, machine learning, and data science in an increasingly connected and data-driven world. The data engineering trends can be divided into Data Infrastructure, Data Architecture, and Data Management categories. Data lineage, data quality, and data discovery tools which are components of Metadata management will merge into the mainstream data management platform.
To drive this unified data management platform, we will see significant adoption of Data mesh principles and vital changes in the data engineering architecture is will include serverless architecture. Cloud data warehouse systems will emerge as an assurance for the future to tightly integrate with data management systems. The uncertainty of object storage engines, storage costs, and the need for specialized hardware will be the norm in the days to come. Data Engineering will play a crucial role in the processes of the future to develop, expand, and deploy new technologies. Enroll in the Data Engineering course with 360DigiTMG to fast-track your career in this data-driven environment.
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
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