phData

JULY 12, 2023

Businesses today are grappling with vast amounts of data coming from diverse sources. To effectively manage and harness this data, many organizations are turning to a data vault—a flexible and scalable data modeling approach that supports agile data integration and analytics.

Clustering 52

Clustering Data Warehouse Data Quality Data Modeling 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? Data is at the core of any ML project, so data infrastructure is a foundational concern. Why did something break? Who did what and when?

ML 144

ML ML Data Scientist AWS 144

phData

AUGUST 16, 2023

In the era of data modernization, organizations face the challenge of managing vast volumes of data while ensuring data integrity, scalability, and agility. What is a Data Vault Architecture? It is agile, scalable, no pre-modeling required, and well-suited for fluid designs. Using dbt is one of the best choices.

Data Warehouse 52

Data Warehouse Data Governance SQL Data Modeling 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

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ETL Data Quality Data Pipeline Data Warehouse 40

phData

AUGUST 10, 2023

Data Vault - Data Lifecycle Architecturally, let’s understand the data lifecycle in the data vault into the following layers, which play a key role in choosing the right pattern and tools to implement. Data Acquisition: Extracting data from source systems and making it accessible.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Pickl AI

JULY 25, 2023

Model Development Data Scientists develop sophisticated machine-learning models to derive valuable insights and predictions from the data. These models may include regression, classification, clustering, and more. They work with databases and data warehouses to ensure data integrity and security.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Server update locks the entire cluster. It supports multiple file formats.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data.

AI 52

AI AI Data Lakes Database 52

phData

APRIL 20, 2023

In today’s world, data-driven applications demand more flexibility, scalability, and auditability, which traditional data warehouses and modeling approaches lack. This is where the Snowflake Data Cloud and data vault modeling comes in handy. What is Data Vault Modeling?

Data Warehouse 72

Data Warehouse Data Governance Clustering Database 72

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines.

AWS 121

AWS Machine Learning Machine Learning ML 121