Full time Internship Training

Data Warehouse ETL:

ETL (Extract, Transform, Load) is a crucial process for data warehousing, allowing businesses to combine data from various sources into a single, centralized repository for analysis and reporting. It involves extracting data from source systems, transforming it to a consistent format, and then loading it into a data warehouse.

Here's a more detailed breakdown:

Extract:

  • Data Acquisition: The first step is to extract data from various sources, which can include databases, cloud storage, APIs, or flat files.
  • Data Selection: Only the relevant data needed for analysis is extracted.

Transform:

  • Data Cleaning and Validation:  This step involves cleaning the data by removing duplicates, correcting errors, and ensuring data integrity. 
  • Data Formatting and Standardization:  Data is transformed to a consistent format, such as changing data types, converting units, and standardizing naming conventions. 
  • Data Aggregation: Data from multiple sources may be aggregated to create summary tables or reports. 

Load:

  • Data Storage: The transformed data is loaded into the data warehouse, which is typically a relational database or a cloud-based data storage system. 
  • Data Indexing and Organization: Data is indexed and organized within the warehouse for efficient querying and retrieval. 

Why ETL is important in data warehousing:

  • Data Consolidation:  ETL allows businesses to combine data from multiple sources, providing a single, comprehensive view of their data. 
  • Data Consistency:
    ETL ensures that data is in a consistent format, making it easier to analyze and report on. 
  • Data Quality:
    ETL helps to improve data quality by cleaning and validating data. 
  • Data Analysis:
    ETL prepares data for analysis, making it easier to extract insights from the data warehouse. 
  • Data Governance:
    ETL processes can be automated and standardized, ensuring that data is managed consistently across the organization. 
In summary, ETL is the backbone of data warehousing, enabling organizations to collect, transform, and load data from various sources into a centralized repository for business intelligence and analytics purposes.
 

 AI integration in data warehousing ETL (Extract, Transform, Load) involves using artificial intelligence and machine learning to automate, optimize, and enhance the traditional data integration process. This integration streamlines ETL workflows, improves data quality, and enables more intelligent data analysis. 

Here’s a breakdown of how AI is integrated into data warehousing ETL:

  • Automated Data Extraction:
    AI can automatically identify and extract data from various sources, including structured and unstructured data, reducing manual effort. 
  • Intelligent Schema Mapping:
    AI algorithms can infer and map data schemas, simplifying the process of integrating data from different sources. 
  • Automated Data Transformation:
    AI can automate data transformation tasks, such as data cleaning, validation, and format conversion, improving data quality and consistency. 
  • Dynamic Pipeline Optimization:
    Machine learning models can monitor ETL pipeline performance and dynamically optimize resource allocation and transformation rules. 
  • Anomaly Detection:
    AI algorithms can identify and flag data inconsistencies, duplicates, and other quality issues, ensuring data accuracy. 
  • Data Enrichment:
    AI can infer additional information and enrich data by adding context and meaning, improving the value of the data for analysis. 
  • Real-time Data Quality Checks:
    AI can perform real-time data quality checks during the ETL process, preventing bad data from entering the data warehouse. 
  • Predictive Analytics:
    AI can analyze historical data in the data warehouse to predict future trends and patterns, enabling proactive decision-making. 
  • Personalized Recommendations:
    AI can analyze customer data in the data warehouse to provide personalized product recommendations and offers. 
  • Automated Reporting and Visualization:
    AI can generate automated reports and visualizations based on data in the warehouse, making it easier for users to gain insights. 
  • Faster Data Integration:
    AI-powered ETL streamlines the data integration process, reducing the time and effort required to onboard new data sources. 
  • Improved Data Quality:
    AI enhances data quality by identifying and resolving data inconsistencies and errors. 
  • Increased Efficiency:
    Automation of ETL tasks frees up data engineers to focus on more strategic initiatives. 
  • Reduced Costs:
    Automating repetitive tasks and optimizing resource utilization can lead to cost savings. 
  • Data Governance:
    Strong data governance practices are crucial to ensure the responsible and ethical use of AI in data warehousing. 
  • Human Expertise:
    While AI can automate many tasks, human expertise is still needed to validate AI-generated results and address complex data issues. 
  • Evolving AI Technologies:
    AI technologies are constantly evolving, so it’s important to stay up-to-date on the latest advancements and best practices. 
  •  Any degree graduates can apply.
  • Must have passed out from 2018 – 2025 graduates and Percentage is not mandatory.
  • Must agree with the company terms and conditions to act professionally and develop the career.
  • Must be able to work anywhere in Karnataka companies in top or reputed companies in every district.
Salary Expectations we can assure for AI Datawarehouse ETL
  • Fresher Level – (0-1 Year): Min 5,00,000 lakhs per year – 6,00,000 lakhs per year 
  • Mid-Level – (1 – 3 year): Min 6,00,000 lakhs per year – 9,00,000 lakhs per year 
  • Senior Level – (3 – 6 year): Min 9,00,000 lakhs per year – 12,00,000 lakhs per year 
Internship Training Hrs./Day: 8 hrs./day (Morning – 4 hrs. & Evening – 4hrs.)
Days in a week: Monday, Tuesday, Thursday & Friday 
Mock Interviews (By MNC companies Staff): Every Wednesday & Saturday: Min 20 mins – Max 30 mins 
Mock Aptitude & Coding tests: Wednesday & Saturday: Min 1 hr. – Max 2 hrs.
Recap of the Training Sessions: Wednesday: 3hrs/day
Internship Practical Training Mode: Online Virtual
* Trainee must have a high-end internet connection during the training period, attend the internship training and present the min 90 % of whole attendance, follow the strict discipline during the training period, complete the practice, assignments and must be updated on time to develop the skills, pass the exams and mock interviews with a percentage of 90% to clear the main four interviews.
* Trainee should be well behaved at all the activities and become a professional at the end of the training.
* Trainees will be selected easily and must join after receiving the joining letter from the company.
 
Internship Training Fees: INR 60,000 Inclusive 18% GST 
Payment Options:
 
Option 1: Without Educational Loan
Installment 1: Down payment of 20%: INR 12,000 at the time of enrollment 
Installment 2: 50% of the remaining balance during 1st week of Internship Training: INR 24,000
Installment 3: Within 21 days from the date of second installment: INR 24,000
No Cash Accepted, Only Company Transaction online payment. 
 
Option 2: With Educational Loan
Apply for the Bajaj Finance in educational training loan process and must have a minimum credit score of 700+ to get approval for the 80% amount of INR 48,000.
Once Approved, Down payment of 20%: INR 12,000 at the time of enrollment and loan processed amount is credited to the company account.
You need to repay back the loan amount in 8 months for a monthly EMI of INR 6,000/month.
 
Once the payment is done, Invoice GST will be shared to you. 
 

Client Hiring Details:

  • We’re associated with fortune 500 companies and the interviews will be scheduled immediately after completion of the training within a week.
  • With over 15 mock interview calls with MNC staff, you’ll ease the interviewer and grab the opportunity to work at high scale in reputed companies.

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