Data Warehouse
A data warehouse is a centralized repository that stores structured, cleaned, and integrated data from multiple source systems, organized specifically to support business intelligence, reporting, and OLAP-style analytical queries.
27 resources across 2 libraries
Glossary Terms(17)
Teradata
Teradata is a massively parallel processing (MPP) relational database platform designed for large-scale enterprise data warehousing and analytics, originally s…
Vertica
Vertica is a columnar, massively parallel processing (MPP) analytics database designed for high-performance querying over very large datasets, originally creat…
Greenplum
Greenplum is an open-source massively parallel processing (MPP) data warehouse database built on top of PostgreSQL, designed for large-scale analytics and busi…
Database Normalization
Database normalization is the process of organizing tables and columns in a relational database to reduce data redundancy and prevent update, insert, and delet…
OLTP
OLTP (Online Transaction Processing) refers to database systems and workloads optimized for large numbers of short, concurrent read/write transactions, such as…
OLAP
OLAP (Online Analytical Processing) refers to database systems and workloads optimized for complex, read-heavy analytical queries — aggregations, trends, and m…
Data Warehouse
A data warehouse is a centralized repository that stores structured, cleaned, and integrated data from multiple source systems, organized specifically to suppo…
Data Lake
A data lake is a centralized storage repository that holds raw data in its native format — structured, semi-structured, or unstructured — without requiring a p…
Data Lakehouse
A data lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the transactional reliability, schema enforcement, and…
ETL
ETL (Extract, Transform, Load) is a data integration process that pulls data from source systems, transforms it into a clean, structured format outside the tar…
ELT
ELT (Extract, Load, Transform) is a data integration pattern that loads raw data into a destination system first and performs transformation afterward, using t…
Change Data Capture (CDC)
Change Data Capture (CDC) is a set of techniques for detecting and capturing row-level inserts, updates, and deletes in a source database as they happen, so th…
Data Mart
A data mart is a focused, subject-oriented subset of a data warehouse, scoped to the needs of a single business function or department such as sales, finance,…
Star Schema
A star schema is a dimensional data modeling technique in which a central fact table, holding measurable business events, is directly connected to multiple den…
Snowflake Schema
A snowflake schema is a dimensional data modeling technique that normalizes dimension tables of a star schema into multiple related sub-tables, reducing data r…
Master Data Management
Master Data Management (MDM) is the discipline of creating and maintaining a single, consistent, authoritative version of an organization's core business entit…
Columnar Database
A columnar database stores data by column rather than by row, allowing analytical queries that scan and aggregate a small number of columns across many rows to…
Interview Questions(10)
What Are Fact and Dimension Tables in a Data Warehouse?
A fact table stores the measurable, numeric events of a business process — like sales amount or quantity sold — along with foreign keys to related dimensions,…
OLAP vs OLTP: What is the Difference?
OLTP (Online Transaction Processing) is optimized for many short, concurrent read/write transactions on individual rows, while OLAP (Online Analytical Processi…
Columnar Storage vs Row Storage: What is the Difference?
Row storage writes all columns of a single record contiguously on disk, making it fast to read or write an entire row at once, while columnar storage groups ea…
Data Lake vs Data Warehouse: What is the Difference?
A data warehouse stores structured, schema-validated data organized for fast, well-defined analytical queries, while a data lake stores raw data of any format…
ETL vs ELT Pipelines: What is the Difference?
ETL (Extract, Transform, Load) transforms data in a separate processing engine before loading it into the target system, while ELT (Extract, Load, Transform) l…
What is Change Data Capture (CDC)?
Change Data Capture (CDC) is a technique for identifying and streaming only the rows that changed — inserts, updates, and deletes — in a source database, typic…
What is a Bitmap Index and When Should You Use One?
A bitmap index stores one bit array per distinct column value, where each bit position corresponds to a row and is set to 1 if that row holds that value, letti…
How Does Run-Length Encoding Work in Columnar Compression?
Run-length encoding (RLE) compresses a column by replacing consecutive repeated values with a single stored value plus a count of how many times it repeats in…
What Are Common Archival Strategies for Old Database Data?
Archival strategies move old, rarely accessed data out of the primary operational database into cheaper long-term storage — via time-based partition archiving,…
Data Lake vs Data Warehouse: What Is the Difference?
A data lake stores raw data of any format — structured, semi-structured, or unstructured — cheaply at scale and applies schema only when the data is read, whil…