Data Engineering with Python

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Data Engineering with Python
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Course Curriculum

Data Engineering with Python Course Content

  1. Introduction to Data Engineering:

    • Overview of data engineering and its role in the data ecosystem
    • Introduction to data engineering tools and technologies
    • Understanding the data engineering workflow and pipeline

  2. Python for Data Engineering:

    • Introduction to Python programming language for data engineering tasks
    • Python libraries for data manipulation and transformation (e.g., pandas)
    • Working with data in different formats (CSV, JSON, XML, etc.) using Python

  3. Data Storage and Retrieval:

    • Relational databases and SQL for data storage and retrieval
    • Connecting Python to databases using libraries such as SQLAlchemy
    • Performing CRUD (Create, Read, Update, Delete) operations with Python and databases

  4. Data Processing and Transformation:

    • Data cleaning, filtering, and transformation using Python
    • Working with large datasets and optimizing data processing tasks
    • Applying data transformation techniques using Python libraries like pandas and NumPy

  5. Data Pipelines and ETL (Extract, Transform, Load):

    • Introduction to data pipelines and ETL processes
    • Building data pipelines using Python libraries (e.g., Apache Airflow)
    • Extracting data from various sources, transforming it, and loading it into target systems

  6. Data Integration and Workflow Management:

    • Working with APIs and web scraping for data integration
    • Automating data workflows and scheduling tasks with Python
    • Managing dependencies and orchestrating data engineering tasks

  7. Data Quality and Testing:

    • Techniques for ensuring data quality and integrity
    • Data validation and data quality checks using Python
    • Testing and debugging data engineering pipelines

  8. Big Data Processing:

    • Introduction to big data concepts and frameworks (e.g., Apache Hadoop, Apache Spark)
    • Processing and manipulating large-scale datasets using Python and big data frameworks
    • Distributed computing and parallel processing with Python for big data processing

  9. Real-Time Data Processing:

    • Handling real-time data streams and event-driven architectures
    • Building real-time data processing pipelines with Python libraries (e.g., Apache Kafka, Apache Flink)
    • Processing and analyzing streaming data using Python

  10. Data Governance and Security:

    • Introduction to data governance principles and practices
    • Ensuring data security and privacy in data engineering processes
    • Compliance with data protection regulations and best practices

Course Overview

Data Engineering with Python Online Training in Hyderabad, Bangalore, India

Data engineering is a field that focuses on the development, construction, and maintenance of data architectures and systems. Python is a popular programming language used in data engineering due to its versatility, extensive libraries, and ease of use. In this context, Python can be used for various data engineering tasks, such as data ingestion, data transformation, data integration, data storage, and data processing.

Here are some common data engineering tasks and how Python can be used for each:

  1. Data Ingestion: Python can be used to retrieve data from various sources, such as databases, APIs, files (CSV, JSON, XML), or streaming platforms. Libraries like requests, pandas, beautifulsoup, and pyspark can help with fetching and parsing data from different sources.

  2. Data Transformation: Python provides powerful libraries like pandas, NumPy, and Dask that enable data transformation and manipulation. You can perform operations like cleaning, filtering, aggregating, joining, and reshaping data using these libraries.

  3. Data Integration: Python can be used to integrate data from multiple sources and systems. Libraries like pandas, Apache Kafka, and Apache Airflow can assist in combining data from different databases, files, or APIs into a unified format.

  4. Data Storage: Python can interact with databases and data storage systems. Libraries like SQLAlchemy, psycopg2, pymongo, and Apache Hadoop provide support for working with various databases like SQL-based databases, NoSQL databases, and distributed file systems.

  5. Data Processing: Python is used for performing data processing tasks, such as batch processing or real-time stream processing. Libraries like pandas, Dask, Apache Spark, and PySpark enable distributed data processing, which can handle large volumes of data efficiently.

  6. Workflow Orchestration: Python frameworks like Apache Airflow help in building and managing complex data workflows. It allows you to define and schedule data pipelines, dependencies, and dependencies between tasks.

  7. Data Quality and Monitoring: Python can be used to implement data quality checks and monitoring mechanisms. Libraries like Great Expectations and Pandas Profiling help in data validation, quality assessment, and generating reports on data statistics.

These are just a few examples of how Python can be used for data engineering tasks. Python's extensive ecosystem of libraries and its general-purpose nature make it a versatile and powerful language for data engineering tasks.

How is Python used in data engineering?

Python is used in data engineering for tasks such as data ingestion, data transformation, data integration, data storage, data processing, workflow orchestration, data quality and monitoring, and machine learning and analytics. Python's versatility, ease of use, and extensive library ecosystem make it a popular choice for data engineers to build robust and scalable data pipelines and systems.

What Python skills are needed for data engineer?

For a data engineer, the following Python skills are essential:

  1. Proficiency in Python: A solid understanding of Python programming language, including its syntax, data types, control flow, functions, and object-oriented programming concepts.

  2. Data Manipulation and Analysis: Strong knowledge of libraries like pandas and NumPy for data manipulation, cleaning, filtering, aggregation, and analysis.

  3. Database Interaction: Familiarity with libraries like SQLAlchemy and knowledge of SQL queries for interacting with relational databases.

  4. Distributed Computing: Understanding of distributed computing frameworks like Apache Spark and PySpark for processing large volumes of data efficiently.

  5. Data Serialization Formats: Knowledge of working with various data serialization formats such as JSON, CSV, XML, and Parquet.

  6. Data Pipeline Development: Experience in building data pipelines using Python libraries like Apache Airflow or similar workflow orchestration tools.

  7. Data Integration: Familiarity with integrating data from different sources using APIs, web scraping, and data extraction techniques.

  8. Version Control and Collaboration: Proficiency in using version control systems like Git for code management and collaboration with other team members.

  9. Debugging and Troubleshooting: Strong problem-solving skills and the ability to debug issues in Python code and data engineering workflows.

  10. Familiarity with Data Storage Technologies: Understanding of databases (SQL and NoSQL), distributed file systems like Hadoop, and cloud storage solutions.

These skills will enable a data engineer to effectively handle data engineering tasks and build scalable and efficient data systems using Python.

Do data engineers use Python or SQL?

Data engineers use both Python and SQL in their work. Python is a general-purpose programming language that is widely used in data engineering for tasks such as data ingestion, data transformation, data integration, data processing, and workflow orchestration. Python provides powerful libraries and frameworks that make it easier to manipulate and process data, interact with various data sources, and build scalable data pipelines.

SQL (Structured Query Language), on the other hand, is a specialized language for managing and querying relational databases. Data engineers often use SQL to interact with databases, perform data extraction, transformation, and loading (ETL) operations, and optimize database queries for data retrieval and storage.

While Python is more versatile and used for a wide range of data engineering tasks, SQL is essential for working with databases and querying structured data. Therefore, data engineers typically have proficiency in both Python and SQL to effectively handle different aspects of their work.

Faq’s

  • There is no specific technology background required.

Our Trainers have highly experience in Support, Implementation and Rollout projects real time solutions on different scenarios and expert in their professionals. BESTWAY Technologies verifies their technical background and experience.

We  record each live class session you undergo through this training and we will share the recordings of each class.

Yes we will schedule a demo class as per the student convenient time by sharing live online streaming access either through Gotomeeting or Webex..

Trainer will provide detailed installation of required Software through Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course. 

If you are enrolled in classes and you have paid fees, but want to cancel the registration for certain reason, it can be done within 48 hours of initial registration. Please make a note that refunds will be processed within 25 days of prior request.

  • We are one of the best Data Engineering with Python online training providers in the world, We have learning Data Engineering with Python customers from India, USA, Singapore, Canada, UK, UAE, Australia, New Zealand, Qatar, South Africa, Malaysia, Saudi Arabia, Mexico, Ireland, Denmark, Sweden and other parts of the world. We are located in India. Offering Online Training in Cities like Hyderabad, Bangalore, Delhi, Mumbai, Chennai, Pune, Kolkata, Ahmedabad, Patna, Jaipur, Lucknow, Kochi, Indore, Chandigarh, Bhopal, SÅ«rat, Kanpur, Coimbatore, Visakhapatnam, Vadodara, Gurgaon, Guwahati, Ludhiana, Allahabad, Nagpur, Noida, Mysore, Ranchi, Bhubaneswar, Faridabad, Raipur, Vijayawada, Jamshedpur, Hubli, Tirupati, Guntur, Kakinada, Rajahmundry, Nellore, Anantapur, Eluru, Warangal, Nizāmābād, Secunderabad, Salem, Trivandrum, kerala, Hubli, Bellary, Gulbarga, Hospet, Tumkur, Thane, Navi Mumbai, Kalyan, Nashik, Aurangabad, Solapur, Gandhinagar, Shenzhen, Hong Kong, Tokyo, Yokohama, Nagoya, Fukuoka, Kobe, Copenhagen, Osaka, Kyoto, Nairobi Kenya, Mombasa, Kisumu, Lagos Nigeria, Ibadan, Abuja, Benin, Sydney, New York, New jersey, Melbourne, Dallas, Adelaide, Perth, Brisbane, London, Paris, Berlin, Vienna, Barcelona, Rome, Madrid, Prague, Munich, Milan, Bucharest, Istanbul, Moscow, Birmingham, Seattle, Baltimore, San Jose, San Marcos, Franklin, Chicago, Philadelphia, Jacksonville, Towson, Minneapolis, Los Angeles, Davidson, Murfreesboro, Houston, San Francisco, Atlanta, Alexandria, San Diego, Washington DC, Sunnyvale, Santa clara, Carlsbad, Tacoma, California, St. Louis, Edison, Raleigh, Nashville, Bellevue, Austin, Charlotte, Garland, Raleigh-Cary, Boston, Salt Lake City, Orlando, Fort Lauderdale, Miami, Gilbert, Tempe, Chandler, Scottsdale, Peoria, Honolulu, Columbus, Plano, Toronto, Montreal, Calgary, Edmonton, Saint John, Vancouver, Richmond, Mississauga, Saskatoon, Kingston, Kelowna, Cape Town, Johannesburg, Durban, Dubbai, Abu Dhabi , Sharjah, Riyadh, Jeddah, Sanaa, Aden, Yemen, Muscat Oman, Kuwait, Doha, Brisbane, Wellington, Auckland, Kuala Lumpur, George Town, Jurong East etc… Hyderabad - Ameerpet, SR Nagar, KPHB, Gachibowli, Dilsukhnagar, madhapur, tarnaka, kukatpally, himayat nagar, Bangalore - Banashankari, Bannerghata Road, Basaveswara Nagar, BTM Layout, Domlur, Electronic city, H S R Layout, Indira Nagar, J P Nagar, Jaya Nagar, K R Puram, Koramangala, Krishnarajapuram, Madivala, Malleswaram, Marathahalli, Mathikere, R T Nagar, Rajaji Nagar, Ramamurthy Nagar, Richmond Road, Shivaji Nagar, Vijaya Nagar, White Field

yes all the training sessions will be a live online streaming using either through gotomeeting or Webex you will be shared with live meeting access while session starts.

Yes, there are some group discount available if group contain more than two.

 

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Reviews

Data Engineering with Python Rated 4.8 based on 4 reviews.

By: Sanaya Khan, Rating:
I am Sanaya for my career, the Data Engineering with Python course had a profound impact. The lecturer passion for Python and data engineering was contagious, and it kept me inspired throughout the course. A complete package for aspiring data engineers, the course addressed crucial subjects like data ingestion, transformation, and loading. I am grateful for the useful skills I acquired and strongly urge anyone wishing to enter the data engineering sector to take this course.

By: Chirag Mehta, Rating:
The Data Engineering with Python course was a fantasy come true for me as a Python enthusiast! Python and data engineering professionals served as the instructors, which greatly enhanced the learning process. I liked how best practises, data governance, and data quality were emphasised. We were prepared for practical data engineering challenges by the course coverage of a variety of data storage and processing systems. I am now comfortable using Python to design and construct data pipelines.

By: Anika Patel, Rating:
The Data engineering with python course was excellent! The professors gave a thorough and lucid overview of Python-based data engineering ideas. The focus on data pipelines, ETL procedures, and data warehousing was much welcomed. Anyone wishing to enter the field of data engineering using Python as their preferred tool would benefit greatly from taking this course!

By: Harish, Rating:
I recently had the privilege of enrolling in the Data Engineering with Python Online Training program at BESTWAY Technologies in Hyderabad, and I can confidently say that it was an exceptional learning experience. This course not only equipped me with the essential skills for data engineering but also exceeded my expectations in various aspects.

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