What is ETL | Extract, Transform and Load | Hadoop Tutorial for Beginners | Hadoop [Part 2]

Understanding ETL in Data Management

Introduction to ETL

• The term ETL stands for Extract, Transform, and Load, a crucial concept in data management.

• Companies often utilize multiple RDBMS (Relational Database Management Systems), such as MySQL and Oracle, to manage their data.

Case Study: ICICI Bank's Data Handling

• ICICI Bank uses Oracle for core banking data and MySQL for CRM (Customer Relationship Management) data.

• The bank has acquired several companies, leading to diverse data formats including XML files from subsidiaries.

Social Media Data Utilization

• ICICI Bank collects social media interaction data, particularly from its Facebook page with around 10,000 daily visitors.

• This social media engagement aids in cross-selling and upselling products based on user interactions.

Analytics and Recommendations

• The bank analyzes user behavior on its platforms to tailor product recommendations effectively.

• By understanding demographics of users interacting with loan offers, the bank can target marketing strategies more efficiently.

Customer Care Insights

• Customer care logs are collected as flat files containing chat logs and call duration metrics.

• Analyzing these logs helps identify customer satisfaction levels based on complaint categories.

Real-Time Customer Engagement Strategy

• Previously ineffective agent-based sales methods prompted ICICI Bank to implement real-time tracking of agents' locations.

• Agents receive nearby customer leads through a tablet application, enhancing efficiency in reaching potential clients.

Diverse Data Sources Challenge

Understanding Data Warehousing and ETL Processes

The Need for a Data Warehouse

• Analyzing data directly from Oracle databases is impractical as they are busy serving customer transactions, making it impossible to run analytics without affecting performance.

• To facilitate analysis, data must be extracted from various sources and centralized into a data warehouse, which serves as a repository for analytical processing.

What is a Data Warehouse?

• A data warehouse is essentially a database that understands SQL but differs in that it does not interact with customers directly; it's designed for internal use.

• The process of populating the data warehouse involves ETL (Extract, Transform, Load), where tools extract data from multiple sources, transform it into a usable format, and load it into the warehouse.

Utilizing Business Intelligence Tools

• Once the data is structured within the warehouse, business intelligence (BI) tools like Tableau can be used to visualize and analyze this information through charts and diagrams.

• Traditional enterprise architecture typically includes disparate data sources connected by ETL tools to ensure all relevant information is available in one location.

Challenges with Traditional ETL Tools

• Popular ETL tools such as Informatica and Pentaho BI connect to various sources to pull data into the warehouse while applying necessary schemas.

• Executives may not have technical skills like SQL or programming; thus, they rely on visualization tools to access insights without needing deep technical knowledge.

Limitations of Current Systems

• If errors occur during the ETL process, it can lead to discrepancies in what executives see versus actual company performance metrics.

• Visualization tools allow CEOs and CTOs to make informed decisions based on accurate representations of consolidated company data stored securely within the warehouse.

Real-Time Data Processing Issues

• One major challenge faced by companies like ICICI Bank was that traditional ETL processes do not operate in real-time; they often run overnight leading to delays in transaction notifications.

• The limitations of single-machine operations restrict how much data can be processed at once; handling terabytes of information becomes unfeasible with conventional setups.

Demand for Immediate Insights

• Customers expect immediate feedback after transactions (e.g., promotional offers), but existing systems cannot provide this due to their batch processing nature.

• Companies need real-time capabilities for capturing transactions instantly so they can offer timely recommendations or promotions based on user activity.

Cost Implications of Data Warehousing

Cost and Alternatives in Data Warehousing

The High Cost of Data Warehousing

• Implementing data warehousing is expensive, making it a significant investment for companies like ICICI Bank, which cannot opt out of using it.

• Companies are seeking cheaper alternatives that provide real-time capabilities, scalability, and the ability to integrate various types of data without relying solely on traditional data warehouses.

Transition to Big Data Solutions

• ICICI Bank approached for a migration to a big data solution due to the high costs associated with their current system; Hadoop was recommended as an alternative.

• The concept of Massive Parallel Processing (MPP) emerged as a potential solution for improving performance in data warehousing systems like Teradata.

Limitations of Parallel Processing

• While parallel processing can be implemented across multiple machines, it still faces limitations regarding physical table division; only logical divisions based on criteria such as country or month are possible.

• Despite improvements from parallel processing, real-time solutions remain elusive due to inherent challenges in performing join operations across divided tables.

Conclusion on Cost and Unstructured Data