Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

Data Pipeline 145

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 145

Pickl AI

OCTOBER 6, 2024

It is a crucial data integration process that involves moving data from multiple sources into a destination system, typically a data warehouse. This process enables organisations to consolidate their data for analysis and reporting, facilitating better decision-making. ETL stands for Extract, Transform, and Load.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

Tableau

APRIL 18, 2022

Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Provide a visual and direct way to combine, shape, and clean data in a few clicks. Ensure the behaves the way you want it to— especially sensitive data and access.

Tableau 102

Tableau Data Quality Analytics Analytics 102

Tableau

APRIL 18, 2022

Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Provide a visual and direct way to combine, shape, and clean data in a few clicks. Ensure the behaves the way you want it to— especially sensitive data and access.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Pickl AI

JULY 25, 2024

In this blog, we’ll delve into the intricacies of data ingestion, exploring its challenges, best practices, and the tools that can help you harness the full potential of your data. Batch Processing In this method, data is collected over a period and then processed in groups or batches.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

JULY 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52