Alation

JUNE 9, 2021

We have an explosion, not only in the raw amount of data, but in the types of database systems for storing it ( db-engines.com ranks over 340) and architectures for managing it (from operational datastores to data lakes to cloud data warehouses). Organizations are drowning in a deluge of data.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

AI 74

AI AI Machine Learning Machine Learning 74

phData

JUNE 11, 2025

Depending on the requirement, it is important to choose between transient and permanent tables, as well as data recovery needs and downtime considerations. By adopting these best practices, organizations can effectively manage Snowflake budgets, optimize credit usage, and drive greater cost efficiency and ROI in their cloud data operations.

Clustering 59

Clustering SQL Database Data Lakes 59

Pickl AI

APRIL 9, 2025

This includes operations like data validation, data cleansing, data aggregation, and data normalization. The goal is to ensure that the data is consistent and ready for analysis. Loading : Storing the transformed data in a target system like a data warehouse, data lake, or even a database.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

phData

FEBRUARY 14, 2023

Data Collector also offers replication and Change Data Capture (CDC) to be able to accurately and efficiently get your data into Snowflake. Data Collector can use Snowflake’s native Snowpipe in its pipelines. Replication of calculated values is not supported during Change Processing.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Furthermore, a shared-data approach stems from this efficient combination. What will You Attain with Snowflake?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

MAY 6, 2024

Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse. Store this data in a customer data platform or data lake.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data Data Quality 52

phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

Data Silos 52

Data Silos Database Data Quality Data Engineering 52

phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account. For greater detail, see the Snowflake documentation.

Clustering 98

Clustering Database SQL Data Pipeline 98

AWS Machine Learning Blog

NOVEMBER 1, 2024

Much of these greenhouse gas emissions can be attributed to travel (such as air travel, hotel, meetings), distribution associated for drugs and documents, and electricity used in coordination centers. Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities.

AWS 111

AWS Data Lakes Machine Learning Machine Learning 111