IBM Journey to AI blog

APRIL 8, 2024

Answer : Along with standard RDS features, Amazon RDS for Db2 supports key Db2 features, such as row and column organized tables for mixed and analytic workloads, the Adaptive Workload Optimizer to for better resource management, and rules-based access controls for advanced data protection.   Scalability 5. . Amazon RDS

AWS 87

AWS Database Data Warehouse Analytics 87

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Alation

MAY 24, 2022

Prime examples of this in the data catalog include: Trust Flags — Allow the data community to endorse, warn, and deprecate data to signal whether data can or can’t be used. Data Profiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape. Book a demo today.

Data Quality 98

Data Quality Data Governance ETL Data Observability 98

Pickl AI

JULY 14, 2024

Regarding retrieval, DBMS utilises query languages like SQL to retrieve information swiftly and accurately based on user requests. Moreover, DBMS systems manage data through functionalities such as indexing, which enhances retrieval speed by logically organising data. Best Data Engineering and SQL Books for Beginners.

Database 52

Database SQL Data Warehouse Data Analyst 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data. My time is up.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data. My time is up.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data. My time is up.

AI 52

AI AI Data Warehouse Machine Learning 52

Alation

JUNE 14, 2023

It’s also the mechanism that brings data consumers and data producers closer together. Our legacy architecture, like that at most organizations, is a massive on-prem enterprise data warehouse,” Lavorini says. “As As we modernize our core banking platforms, the data goes with that modernization journey.”

Data Pipeline 52

Data Pipeline ETL Data Warehouse SQL 52

Mlearning.ai

AUGUST 18, 2023

Understanding SQL Index: The Key to Faster Query Execution Exploring SQL Index: Basics, Types, Benefits, and Query Optimization Image From Pexels Index is a very crucial topic in SQL. Indexes are the silent partners in SQL, working behind the scenes to make queries sing. What is the Index in SQL and need for it?

SQL 52

SQL Clustering Database Machine Learning 52

phData

SEPTEMBER 27, 2024

Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models. It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52