phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

OCTOBER 21, 2024

It manipulates data using SQL (Structured Query Language). NoSQL DBMS NoSQL systems are designed to handle unstructured and semi-structured data. They provide flexibility in data models and can scale horizontally to manage large volumes of data. Famous examples include MySQL , PostgreSQL, and Oracle.

Database 52

Database Apache Kafka Machine Learning Machine Learning 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. Uses secure protocols for data security. Cons Limited connectors.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. In contrast, such traditional query languages struggle to interpret unstructured data. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52