Data Warehouses and Data Lakes Overview

Data Warehouses and Data Lakes

• A data warehouse aggregates data from multiple sources into a consistent store for analytics.
• Data warehouses support data analysis, mining, artificial intelligence, machine learning, front-end reporting, and OLAP (online analytical processing).
• Traditionally, data warehouses were hosted on-premises within enterprise data centers, initially on mainframes, then Unix, Windows, and Linux systems.

Data Warehouse Hosting

• In the 2000s, the growth of large datasets and emergence of specialized systems prompted data analysis to be performed on-premises.
• Data warehouses are now also increasingly hosted on cloud platforms.

Cloud Data Warehouses

• Cloud data warehouses emerged as a scalable, pay-as-you-go service, eliminating hardware purchases.
• Cloud data warehouse solutions are suitable for various uses, including equipment needs, staffing requirements, banking, financial technology (fin-tech), risk evaluation, fraud detection, and cross-selling services.

Data Warehouse Benefits

• Consolidates data from diverse sources into a single source of truth.
• Improves speed of access with all available data.
• Aids in faster business decision-making with insightful data.
• Enhances data quality.
• Provides smarter business decisions through support by business intelligence.

Data Warehouse Advantages and Summary

• Data warehouses consolidate data from various sources into a single, consistent data store.
• Data warehouses support data mining, AI, machine learning, OLAP, and front-end reporting.
• Data warehouses help organizations enhance data quality, improve insights, and facilitate better decision-making. This, in turn, leads to improved competitive advantages gained through better quality in business operations.

Data Marts

• Data marts are subsets of data warehouse data used for specific business areas.
• Provide efficient support for tactical decision-making.
• Data marts can help end-users quickly focus on relevant data and reduce time spent searching for necessary information within larger data warehouses.
• Typically structured as relational databases with a star or snowflake schema.
• Commonly includes a central fact table containing business metrics and surrounding dimension tables for additional information.
• Data mart types include dependent, independent, and hybrid models.

Data Mart Pipelines

• Data loading processes, called 'pipelines', transfer data into data marts.
• Pipelines bring data from different sources, then transform and clean it before loading it into the destination data mart.
• Appropriate ETL (extract, transform, load) processes are crucial to move data to the selected location efficiently and reliably.

Data Marts vs. Data Warehouses

• Data warehouses are larger repositories with strategic scope, while data marts are smaller repositories focused on tactical decision-making.
• Data warehouses provide an exhaustive data history for a wide set of business areas, while data marts offer a more concentrated, and in-depth perspective on specific business areas.
• Independent data marts stand alone, requiring distinct planning and extra features, while dependent data marts inherit security features of the enterprise data warehouse (EDW).
• Independent data marts require custom ETL processes while dependent data marts inherit data pipelines from the EDW, leading to simpler integration processes.

Data Lakes

• Data lakes are repositories for raw, unprocessed data from various structured, semi-structured and unstructured sources.
• No rigid structure or schema is required for the data, allowing it to be stored in its native format.
• Data lakes provide flexibility for different needs with more scalability than data warehouses.
• Data is loaded into a data lake in its original form and can be processed and transformed for different uses later.

Data In Data Lakes

• Data lakes efficiently store the totality of data sources without immediate structure demands.
• This flexibility is ideal for situations where the intended use cases are unclear, or even unknown beforehand.

Data Lake Benefits

• Handles all types of data (structured, semi-structured, unstructured).
• Offers scalable data storage capacity.
• Data can be easily adapted for various uses.
• Saves time, since schema definition and transformation does not occur beforehand.

Data Lake Vendors

• Several vendors offer data lake solutions on cloud platforms, including Amazon, Microsoft, Google, Cloudera, and others.

Data Lake vs. Data Warehouse

• Data lakes are usually more flexible than data warehouses and are loaded with raw data.
• Data warehouses must meet strict quality thresholds before loading, and need a strict schema definition.
• Data lakes load all types of data directly; data warehouses need pre-processed data to be loaded.

Explore the fundamental concepts of data warehouses and data lakes in this quiz. Learn about their roles in data aggregation, analytics, and the transition from on-premises to cloud hosting solutions. Discover the benefits and applications of cloud data warehouses in various industries.