Frequently Asked Questions

Business Intelligence (BI) refers to technologies and practices for the collection, integration, analysis, and presentation of business data, supporting informed business decision-making. Analytics involves the use of data, statistical analysis, and predictive modeling to identify patterns, trends, and relationships, including descriptive, predictive, and prescriptive analytics.

Business Intelligence (BI):

● Focuses on what has happened and what is currently happening.
● Utilizes reporting, dashboards, and data visualization.

Business Analytics:

● Involves descriptive, predictive, and prescriptive analytics.
● Uses statistical models to predict future outcomes and recommend actions.


Qlik is a data visualization and business intelligence platform used for creating interactive dashboards and visualizations, facilitating data exploration and discovery.

Tableau is a data visualization tool used to create visual representations of data, such as charts and dashboards, helping in data analysis and sharing insights through visual storytelling.

Data governance in ETL refers to the management of data availability, usability, integrity, and security. It ensures data processes comply with policies and standards, maintaining data quality and consistency throughout the data lifecycle.

  • Data Quality: Ensuring accuracy and reliability of data.
  • Data Management: Handling, storage, and lifecycle management.
  • Data Policies: Rules and guidelines for data usage.
  • Data Security: Protecting data from unauthorized access.
  • Data Compliance: Meeting regulatory and legal requirements.
  • Data Stewardship: Responsibility for managing data assets.

Data transformation in ETL is the process of converting data into a format suitable for analysis and reporting. This includes cleaning, filtering, aggregating, and enriching data, ensuring data consistency and usability.

  • Data Cleaning: Removing errors and inconsistencies.
  • Data Integration: Combining data from different sources.
  • Data Aggregation: Summarizing data.
  • Data Normalization: Standardizing data formats.
  • Data Enrichment: Adding additional information to data.
  • Data Filtering: Selecting relevant data.

A data warehouse is a centralized repository for large volumes of structured data from multiple sources, supporting business intelligence and data analysis. An example is Amazon Redshift, a managed data warehouse service for analyzing large datasets using SQL.

  • Enterprise Data Warehouse (EDW): Centralized for the entire organization.
  • Operational Data Store (ODS): Stores current operational data.
  • Data Mart: Focuses on specific business lines or departments.

Azure Data Lake is a scalable data storage and analytics service by Microsoft Azure, designed for big data analytics. It supports frameworks like Hadoop, Spark, and Azure Synapse Analytics.

Snowflake is a cloud-based data warehousing solution that facilitates data storage, processing, and analytics. It supports data sharing, real-time processing, and integration with various data tools.

Data Ingestion: The process of importing, transferring, loading, and processing data from various sources into a storage medium for further use.


  • Apache Kafka
  • Apache Nifi
  • AWS Glue
  • Google Cloud Dataflow
  • Talend

A centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics to guide better decisions.

  • Apache Kafka
  • Amazon Kinesis
  • Google Cloud Pub/Sub
  • Apache Flink
  • Apache Storm

ETL (Extract, Transform, Load):

  • Informatica PowerCenter
  • Talend
  • Apache Nifi
  • Microsoft SQL Server Integration Services (SSIS)

ELT (Extract, Load, Transform):

  • Snowflake
  • Amazon Redshift
  • Google BigQuery
  • Databricks
  • Datadog
  • Monte Carlo
  • Bigeye
  • Datafold
  • Splunk

AI (Artificial Intelligence) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

ML (Machine Learning) is a subset of AI that involves the development of algorithms that allow computers to learn from and make predictions or decisions based on data. ML enables systems to improve their performance over time without being explicitly programmed.

Applications in Business:

  • Customer Service: AI-powered chatbots and virtual assistants handle customer inquiries and support.
  • Data Analysis: ML algorithms analyze large datasets to identify trends, make predictions, and inform strategic decisions.
  • Marketing: Personalized recommendations and targeted advertising are driven by AI analyzing customer behavior and preferences.
  • Operations: AI optimizes supply chain management, inventory control, and predictive maintenance.
  • Finance: AI detects fraudulent activities, automates trading, and enhances risk management.
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Apache MXNet
  • Google Cloud AI Platform
  • AWS SageMaker
  • Microsoft Azure Machine Learning