Monte Carlo

FEBRUARY 6, 2023

But in reality, a data warehouse migration to cloud solutions like Snowflake and Redshift requires a tremendous amount of preparation to be successful—from schema changes and data validation to a carefully executed QA process. What’s more, issues in the source data could even be amplified by a new, sophisticated system.

Data Warehouse 52

Data Warehouse AWS Data Validation Data 52

Monte Carlo

JANUARY 25, 2023

In those cases, we try to test on a blank or sample of data. Schema compatibility We use the Confluent (Kafka) Schema Registry to store contracts for the data warehouse. Data warehouse setup We define our data warehouse in code using an open-source Python tool called dbt (data build tool).

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

ProjectPro

JANUARY 31, 2023

Step 4: Data Transformation and Enrichment Data transformation involves changing the format or value inputs to achieve a specific result or to make the data more understandable to a larger audience. Enriching data entails connecting it to other related data to produce deeper insights. Is SQL Good for Big Data?

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40