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

phData

JULY 12, 2023

Understanding Data Vault Architecture Data vault architecture is a data modeling and data integration approach that aims to provide a scalable and flexible foundation for building data warehouses and analytical systems.

Clustering 52

Clustering Data Warehouse Data Quality Data Modeling 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

phData

JUNE 26, 2024

Cleaning and preparing the data Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction. Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineering 52

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

phData

JANUARY 7, 2025

Structuring the dbt Project The most important aspect of any dbt project is its structural design, which organizes project files and code in a way that supports scalability for large data warehouses. Other models should reference the cleaned data from the staging model rather than the raw source.

SQL 52

SQL Data Warehouse Database Data Models 52

phData

NOVEMBER 4, 2024

This accessible approach to data transformation ensures that teams can work cohesively on data prep tasks without needing extensive programming skills. With our cleaned data from step one, we can now join our vehicle sensor measurements with warranty claim data to explore any correlations using data science.

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69

Alation

JULY 12, 2022

Many things have driven the rise of the cloud data warehouse. The cloud can deliver myriad benefits to data teams, including agility, innovation, and security. More users can access, query, and learn from data, contributing to a greater body of knowledge for the organization. Build Out a Data Synchronization Process.

Cloud Data 52

Cloud Data Data Warehouse Database Machine Learning 52

phData

NOVEMBER 13, 2023

Read more about the dbt Explorer: Explore your dbt projects dbt Semantic Layer: Relaunch The dbt Semantic Layer is an innovative approach to solving the common data consistency and trust challenges. Tableau (beta) Google Sheets (beta) Hex Klipfolio PowerMetrics Lightdash Mode Push.ai

Database 52

Database Business Intelligence Business Intelligence Data Silos 52