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Advanced Data Engineering Program

Duration: 4 Months
Course Type: Offline Sessions

            Our Advanced Data Analytics Training Program is tailored for data enthusiasts, analysts, and professionals looking to deepen their expertise in the realm of data analytics. This program is designed to refine participants’ skills, offering advanced knowledge in data interpretation, statistical analysis, machine learning, and big data technologies. With a focus on real-world applications, participants gain hands-on experience, enabling them to extract actionable insights, predict trends, and solve complex business problems.

Key Features

  1. 100% Job Guaranteed: The candidates who are admitted for the training program will be directly absorbed by Zestlogic Systems
  2. Comprehensive Curriculum: A carefully crafted curriculum covering essential theoretical knowledge and hands-on practical skills in the chosen field.
  3. Expert Instruction: Training led by experienced instructors and industry experts, offering in-depth insights and real-world examples to enhance learning.
  4. Hands-on Experience: Practical, hands-on sessions and projects allowing participants to apply the learned concepts in real-world scenarios, reinforcing understanding and skill development.
  5. Interactive Learning: Engaging workshops, group discussions, and interactive sessions fostering collaboration, problem-solving, and knowledge exchange among participants.
  6. Industry-Relevant Content: Training content designed to align with current industry trends, best practices, and emerging technologies, ensuring participants receive relevant and up-to-date knowledge.
  7. International Projects: Opportunities to work on International projects or case studies, allowing participants to apply their skills to solve practical challenges and build a portfolio of work.

Key Topics

  1. Data Integration Tools:
    • Apache NiFi: An open-source data integration tool for moving data between systems.
    • Apache Kafka: A distributed streaming platform for data integration.
    • Talend: An open-source ETL tool for data integration and transformation.
  2. Data Storage and Databases:
    • Amazon S3: Amazon’s cloud storage service for object storage.
    • Hadoop Distributed File System (HDFS): A distributed file system for big data storage.
    • Apache Cassandra: A NoSQL database for high-scale, fault-tolerant data storage.
    • Apache Spark: A fast and general-purpose cluster computing system with data storage capabilities.
  3. Data Processing and Transformation Tools:
    • Apache Spark: A powerful tool for big data processing and transformation.
    • Apache Hive: A data warehousing infrastructure built on top of Hadoop for querying and analysis.
    • Apache Beam: An open-source, unified model for defining both batch and stream data processing pipelines.
  4. Data Pipeline Orchestration Tools:
    • Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows.
    • Luigi: A Python package to build complex pipelines of batch jobs.
    • AWS Glue: A fully managed extract, transform, and load (ETL) service.
  5. Data Quality and Profiling Tools:
    • Trifacta: A data wrangling tool for cleaning and preparing data for analysis.
    • Great Expectations: An open-source library for data quality assessment.
    • Talend Data Quality: A tool for data quality assessment and management.
  6. Data Warehousing and Analytics:
    • Amazon Redshift: A data warehousing service for running complex queries on large datasets.
    • Google BigQuery: A serverless, highly scalable data warehousing solution.
    • Snowflake: A cloud-based data warehousing platform.
  7. Version Control and Collaboration:
    • Git: A version control system for tracking changes in code and configuration.
    • GitLab: A web-based Git repository manager that provides source code management and CI/CD features.
    • Apache Subversion (SVN): A centralized version control system.
  8. Database Query Languages:
    • SQL: Structured Query Language for querying and managing relational databases.
    • NoSQL: Databases like MongoDB and Cassandra for handling unstructured or semi-structured data.
Want to know more?

We understand the importance of approaching each work integrally and believe in the power of simple.

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