Power bi training
in
Hyderabad
” content_phone=”Power bi training
in
Hyderabad
” content_last_edited=”on|phone” _builder_version=”4.27.0″ _module_preset=”d9095885-6be8-489d-b84e-4110b5cebf5e” header_font_size=”72px” custom_margin=”||10px||false|false” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]Data Engineering Training
[/et_pb_text][et_pb_button button_text=”Book for Demo” _builder_version=”4.27.0″ _module_preset=”cf324c39-0c42-4581-ba95-9482883339d4″ locked=”off” global_colors_info=”{}”][/et_pb_button][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Course Chapters” _builder_version=”4.22.0″ _module_preset=”default” collapsed=”on” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.22.0″ _module_preset=”default” max_width=”1280px” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][et_pb_column type=”2_3″ _builder_version=”4.22.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.0″ _module_preset=”197d5681-3118-404e-b7a1-59e754646a59″ custom_margin=”||10px||false|false” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″]Course Content for Data Engineering training
[/et_pb_text][et_pb_accordion _builder_version=”4.27.0″ _module_preset=”1abede15-2e71-49fc-876a-2d18b915bf95″ hover_enabled=”0″ locked=”off” global_colors_info=”{}” border_color_all=”#f4f4f4″ border_width_all=”2px” sticky_enabled=”0″][et_pb_accordion_item _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}” open=”on”]Data Engineering with PySpark, Databricks, and GCP: Mastering the Data Pipeline
Course Tagline
“From Data to Insights: Build Your Career in Modern Data Engineering”
Key Selling Points
- Comprehensive curriculum covering in-demand skills
- Hands-on experience with industry-standard tools
- Learn from basics to advanced concepts in just 60 hours
- Practical, job-ready skills for the data-driven world
Target Audience
- Aspiring data engineers
- Software developers looking to transition to data engineering
- Data analysts seeking to expand their technical skills
- Any graduates in computer science or related fields
Course Highlights
- Master Python for data manipulation and analysis
- Dive deep into SQL for efficient data querying and management
- Understand big data concepts and the Hadoop ecosystem
- Harness the power of Apache Spark with PySpark
- Explore Databricks for collaborative data engineering
Get introduced to cloud computing with Google Cloud Platform
[/et_pb_accordion_item][et_pb_accordion_item title=”Subtopics” _builder_version=”4.27.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” open=”off” sticky_enabled=”0″]Detailed Data Engineering Course Syllabus
Module 1: Introduction to Data Engineering (5 hours)
1.1 What is Data Engineering? (1 hour)
- Definition and scope of data engineering
- Differences between data engineers, data scientists, and data analysts
- Key responsibilities of a data engineer
- Evolution of data engineering
1.2 Data Engineering Lifecycle (2 hours)
- Data generation and collection
- Sources of data (APIs, databases, logs, etc.)
- Data ingestion techniques
- Data storage and warehousing
- Types of data storage (relational, NoSQL, data lakes)
- Data modeling concepts
- Data processing and transformation
- Batch vs. stream processing
- ETL vs. ELT
- Data analysis and visualization
- Role of data engineers in supporting analytics
- Data quality and governance
1.3 Data Engineering Tools and Technologies Overview (2 hours)
- Databases and data warehouses
- Relational databases (MySQL, PostgreSQL)
- NoSQL databases (MongoDB, Cassandra)
- Data warehouses (Snowflake, Amazon Redshift)
- Big data technologies
- Hadoop ecosystem
- Apache Spark
- Distributed file systems (HDFS)
- Cloud platforms
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
- ETL/ELT tools
- Apache NiFi
- Talend
- Airflow
Module 2: Python for Data Engineering (10 hours)
2.1 Python Basics (3 hours)
- Python installation and environment setup
- Variables and data types
- Numeric types (int, float)
- Strings
- Booleans
- Operators (arithmetic, comparison, logical)
- Control structures
- if-else statements
- for and while loops
- Functions
- Defining and calling functions
- Arguments and return values
- Lambda functions
- Modules and packages
- Importing modules
- Creating custom modules
2.2 Data Structures in Python (3 hours)
- Lists
- Creating and accessing lists
- List methods (append, extend, insert, remove)
- List comprehensions
- Tuples
- Immutability and use cases
- Dictionaries
- Creating and accessing dictionaries
- Dictionary methods
- Nested dictionaries
- Sets
- Set operations (union, intersection, difference)
- Arrays
- NumPy arrays basics
2.3 File Handling and I/O Operations (2 hours)
- Opening and closing files
- Reading from files (read, readline, readlines)
- Writing to files
- Working with CSV files
- csv module
- Reading and writing CSV
- JSON handling
- json module
- Parsing and creating JSON
2.4 Python for Data Manipulation (2 hours)
- Introduction to NumPy
- NumPy array operations
- Mathematical functions
- Pandas basics
- Series and DataFrames
- Reading and writing data (CSV, Excel, SQL)
- Basic data manipulation (filtering, sorting, grouping)
Module 3: SQL for Data Engineering (10 hours)
3.1 Relational Database Concepts (2 hours)
- Database design principles
- Normalization
- Primary and foreign keys
- Tables, rows, and columns
- Relationships (one-to-one, one-to-many, many-to-many)
- ACID properties
- Atomicity
- Consistency
- Isolation
- Durability
- Indexing basics
3.2 Basic SQL (3 hours)
- SELECT statements
- Selecting specific columns
- Aliasing
- Filtering with WHERE
- Comparison operators
- Logical operators (AND, OR, NOT)
- LIKE and IN operators
- Sorting with ORDER BY
- Ascending and descending order
- Sorting by multiple columns
- Aggregations with GROUP BY
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- HAVING clause
3.3 Joins and Subqueries (3 hours)
- Types of JOINs
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- FULL OUTER JOIN
- Self-joins
- Writing and using subqueries
- Subqueries in WHERE clause
- Subqueries in FROM clause
- Correlated subqueries
3.4 Advanced SQL (2 hours)
- Window functions
- ROW_NUMBER, RANK, DENSE_RANK
- Partitioning and ordering
- Common Table Expressions (CTEs)
- Views
- Creating and using views
- Materialized views
- Stored procedures
- Creating and calling stored procedures
- Transactions
- BEGIN, COMMIT, ROLLBACK
Module 4: Introduction to Hadoop and Distributed Computing (5 hours)
4.1 Big Data Concepts (1 hour)
- Characteristics of Big Data (5 V’s)
- Volume
- Velocity
- Variety
- Veracity
- Value
- Challenges in handling Big Data
- Use cases for Big Data
4.2 Hadoop Ecosystem Overview (2 hours)
- Hadoop Distributed File System (HDFS)
- Architecture (NameNode, DataNode)
- Read and write operations
- MapReduce paradigm
- Map phase
- Shuffle and sort
- Reduce phase
- YARN (Yet Another Resource Negotiator)
- Resource Manager
- Node Manager
- Other Hadoop ecosystem components
- Hive
- HBase
- Pig
4.3 Distributed Computing Principles (2 hours)
- Parallel processing
- Task parallelism vs. data parallelism
- Data partitioning and distribution
- Sharding strategies
- Fault tolerance
- Replication
- Recovery mechanisms
- High availability
- Active-passive vs. active-active setups
- CAP theorem
Module 5: Apache Spark and PySpark (15 hours)
5.1 Apache Spark Fundamentals (3 hours)
- Spark architecture
- Driver program
- Cluster manager
- Executor processes
- Spark execution model
- Jobs, stages, and tasks
- DAG (Directed Acyclic Graph)
- RDDs (Resilient Distributed Datasets)
- Creating RDDs
- Transformations and actions
- Persistence and caching
5.2 PySpark Basics (4 hours)
- Setting up PySpark
- Installation
- Configuring Spark with Python
- SparkContext and SparkSession
- Creating a SparkSession
- Configuring Spark properties
- RDD operations in PySpark
- map, filter, reduce
- flatMap, distinct, sample
- union, intersection, subtract
- Pair RDDs and key-value operations
5.3 Working with DataFrames and Datasets (4 hours)
- Creating and manipulating DataFrames
- From RDDs, files, and external sources
- Schema definition and inference
- StructType and StructField
- Handling nested schemas
- DataFrame transformations and actions
- select, filter, groupBy
- join, union, distinct
- agg, pivot, melt
- Datasets and strong typing in Scala (brief overview)
5.4 Advanced PySpark (4 hours)
- Window functions
- Ranking, running totals, moving averages
- User-Defined Functions (UDFs)
- Creating and registering UDFs
- Vectorized UDFs for performance
- Performance tuning and optimization
- Caching and persistence strategies
- Broadcast variables and accumulators
- Partitioning and coalesce
- Spark SQL and Catalog API
Module 6: Databricks using Spark (10 hours)
6.1 Introduction to Databricks (2 hours)
- Databricks architecture
- Control plane and data plane
- Integration with cloud services
- Databricks workspace
- Notebooks, dashboards, and libraries
- Collaborative features
- Version control and sharing
6.2 Working with Databricks Clusters (2 hours)
- Cluster creation and management
- Cluster modes (Standard, High Concurrency, Single Node)
- Autoscaling and spot instances
- Configuring and optimizing clusters
- Spark configurations
- Driver and executor settings
6.3 Databricks Features for Spark (4 hours)
- Delta Lake integration
- ACID transactions on data lakes
- Time travel and versioning
- Schema enforcement and evolution
- MLflow for machine learning lifecycle
- Experiment tracking
- Model registry
- Model serving
- Databricks SQL warehouses
- Creating and managing SQL warehouses
- Query editor and visualization
6.4 Databricks Workflows and Jobs (2 hours)
- Creating and scheduling jobs
- Task dependencies
- Parameterization
- Monitoring and managing workflows
- Job clusters
- Notifications and alerts
- Delta Live Tables
- Declarative ETL
- Data quality checks
Module 7: Introduction to Google Cloud Platform (GCP) (5 hours)
7.1 GCP Basics (2 hours)
- GCP account setup
- Creating a project
- Billing setup
- GCP console navigation
- Cloud Shell
- Cloud SDK
- Overview of key GCP services
- Compute (Compute Engine, App Engine)
- Storage (Cloud Storage, Cloud SQL)
- Networking (VPC, Cloud DNS)
7.2 GCP for Data Engineering (3 hours)
- BigQuery for data warehousing
- Loading data into BigQuery
- Writing and optimizing queries
- BigQuery ML basics
- Cloud Storage for object storage
- Buckets and objects
- Access control and lifecycle management
- Cloud Dataproc for managed Spark and Hadoop
- Creating and managing Dataproc clusters
- Submitting Spark jobs
- Introduction to Cloud Dataflow
- Apache Beam programming model
Batch and streaming pipelines
[/et_pb_accordion_item][et_pb_accordion_item title=”Prerequisites” _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}” open=”off”]Who Should Attend?
Business Analysts looking to enhance their data analysis skills
Data Professionals aiming to leverage Power BI for advanced analytics
Managers and Executives who want to make data-driven decisions
Anyone interested in learning how to visualize and analyze data effectively
[/et_pb_accordion_item][et_pb_accordion_item title=”Market opportunities” _builder_version=”4.27.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” open=”off” sticky_enabled=”0″]Positions and hierarchy of Data Engineering :
Summary of Levels and Titles:
- Entry-Level: Data Engineering Intern, Junior Data Engineer
- Mid-Level: Data Engineer, ETL Developer, Cloud Data Engineer
- Senior-Level: Senior Data Engineer, Data Architect, Big Data Engineer
- Lead & Managerial: Lead Data Engineer, Data Engineering Manager, ETL Manager
- Director & Executive-Level: Director of Data Engineering, VP of Data Engineering, Chief Data Officer (CDO)
Each level not only involves more technical responsibility but also a shift toward leadership, strategic planning, and cross-functional collaboration. As data engineering continues to grow, so do the career opportunities within this field.
[/et_pb_accordion_item][et_pb_accordion_item title=”Certifications” _builder_version=”4.27.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” open=”off” sticky_enabled=”0″]Levels of certification available for power bi:
Google Professional Data Engineer certification link
https://cloud.google.com/learn/certification/data-engineer
AWS Certified Data Analytics – Specialty
https://aws.amazon.com/certification/
Microsoft Certified: Azure Data Engineer Associate
Databricks Certified Data Engineer Associate
[/et_pb_accordion_item][et_pb_accordion_item title=”Salary range” open=”off” _builder_version=”4.27.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]
Minimum and maximum salaries for Data engineering:
Data engineering continues to be a high-demand field, with significant salary growth opportunities as professionals gain more experience and technical skills.
1. Entry-Level Data Engineer Salaries
- Average Salary: $70,000 – $100,000 per year
- Junior Data Engineer: Typically, individuals with 0-2 years of experience start at this level.
- Factors: Geographic location, company size, and the complexity of the tools being used (cloud platforms, big data solutions).
- U.S. Cities:
- San Francisco: $90,000 – $110,000
- New York: $85,000 – $105,000
- Austin: $75,000 – $95,000
- Seattle: $85,000 – $100,000
- International:
- United Kingdom: £35,000 – £50,000
- Germany: €50,000 – €65,000
- India: ₹6,00,000 – ₹10,00,000
- Australia: AU$75,000 – AU$95,000
2. Mid-Level Data Engineer Salaries
- Average Salary: $100,000 – $140,000 per year
- Data Engineer: With 2-5 years of experience, professionals in this bracket have honed their skills in data pipeline construction, cloud services, and automation.
- U.S. Cities:
- San Francisco: $120,000 – $150,000
- New York: $110,000 – $140,000
- Austin: $95,000 – $125,000
- Seattle: $110,000 – $140,000
- International:
- United Kingdom: £50,000 – £70,000
- Germany: €65,000 – €85,000
- India: ₹10,00,000 – ₹20,00,000
- Australia: AU$100,000 – AU$130,000
3. Senior Data Engineer Salaries
- Average Salary: $140,000 – $180,000 per year
- Senior Data Engineer: With 5+ years of experience, these professionals design complex, scalable data systems, optimize performance, and take on leadership roles.
- U.S. Cities:
- San Francisco: $160,000 – $190,000
- New York: $140,000 – $180,000
- Austin: $120,000 – $160,000
- Seattle: $140,000 – $180,000
- International:
- United Kingdom: £70,000 – £90,000
- Germany: €85,000 – €110,000
- India: ₹20,00,000 – ₹35,00,000
- Australia: AU$130,000 – AU$160,000
4. Lead Data Engineer / Manager Salaries
- Average Salary: $160,000 – $200,000 per year
- Lead Data Engineer / Data Engineering Manager: These professionals are responsible for leading data engineering teams, guiding technical strategies, and making key architectural decisions.
- U.S. Cities:
- San Francisco: $180,000 – $230,000
- New York: $170,000 – $210,000
- Austin: $140,000 – $180,000
- Seattle: $170,000 – $210,000
- International:
- United Kingdom: £90,000 – £120,000
- Germany: €110,000 – €140,000
- India: ₹35,00,000 – ₹50,00,000
- Australia: AU$150,000 – AU$180,000
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What’s Included
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[/et_pb_text][et_pb_text _builder_version=”4.27.0″ _module_preset=”ed3f6bdd-6f0a-4f57-b875-b91c6b9a7d2f” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″]Basic Python
Basic Java
Basics of SQL
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