Monte Carlo

AUGUST 8, 2023

The Definitive Guide to Data Validation Testing Data validation testing ensures your data maintains its quality and integrity as it is transformed and moved from its source to its target destination. It’s also important to understand the limitations of data validation testing.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

ProjectPro

FEBRUARY 8, 2023

Data Integration and Transformation, A good understanding of various data integration and transformation techniques, like normalization, data cleansing, data validation, and data mapping, is necessary to become an ETL developer. Extract, transform, and load data into a target system.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Monte Carlo

NOVEMBER 20, 2023

A data migration is the process where old datasets, perhaps resting in outdated systems, are transferred to newer, more efficient ones. Sure, you’re moving data from point A to point B, but the reality is far more nuanced. You have to ensure that data remains intact and consistent during the migration process.

MongoDB 52

MongoDB ETL Tools Database Data 52

Ascend.io

AUGUST 31, 2023

Performance: Because the data is transformed and normalized before it is loaded , data warehouse engines can leverage the predefined schema structure to tune the use of compute resources with sophisticated indexing functions, and quickly respond to complex analytical queries from business analysts and reports.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Knowledge Hut

JANUARY 29, 2024

To excel in this domain, individuals should possess the following skills: Data Transformation: The ability to perform various data transformations, such as merging, ordering, and aggregating, to prepare data for analysis. What are the six steps of data wrangling? What is data wrangling vs data cleaning?

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Databand.ai

JUNE 14, 2023

Deequ Deequ, an open-source library created by Amazon Web Services (AWS), enables testing the completeness, consistency, and accuracy of large-scale datasets using declarative constraints expressed in SQL-like syntax. Learn more in our detailed guide to data quality framework Better data observability equals better data reliability.

Amazon Web Services 52

Amazon Web Services Datasets High Quality Data ETL Tools 52

Knowledge Hut

APRIL 19, 2023

Identify source systems and potential problems such as data quality, data volume, or compatibility issues. Step 2: Extract data: extracts the necessary data from the source system. This API may include using SQL queries or other data mining tools. It supports various data sources and formats.

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52