Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. 5 Benefits of Data Modernization. Advanced Tooling.

Data Governance 52

Data Governance Cloud Data Database Data Silos 52

Alation

JUNE 21, 2022

Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data scientists went beyond database tables to data lakes and cloud data stores. Data scientists want to catalog not just information sources, but models.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

Precisely

JANUARY 19, 2023

Data Integration Enterprises are betting big on analytics, and for good reason. The volume, velocity, and variety of data is growing exponentially. Platforms like Hadoop and Spark prompted many companies to begin thinking about big data differently than they had in the past.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

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 5, 2024

Setting up the Information Architecture Setting up an information architecture during migration to Snowflake poses challenges due to the need to align existing data structures, types, and sources with Snowflake’s multi-cluster, multi-tier architecture. Moving historical data from a legacy system to Snowflake poses several challenges.

SQL 52

SQL Database Data Quality Data Warehouse 52

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. This produces end-to-end lineage so business and technology users alike can understand the state of a data lake and/or lake house.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

Alation

AUGUST 11, 2022

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. And now with some of these cloud data warehouses becoming such behemoths, everything is getting centralized again.

Data Warehouse 52

Data Warehouse Data Engineering Data Engineer Data Engineering 52

phData

SEPTEMBER 20, 2023

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. What are the Advantages and Disadvantages of Data Mesh?

Data Silos 52

Data Silos Database Data Quality Data Engineering 52

Dataversity

MARCH 22, 2021

This announcement is interesting and causes some of us in the tech industry to step back and consider many of the factors involved in providing data technology […]. The post Where Is the Data Technology Industry Headed? Click here to learn more about Heine Krog Iversen.

Data Lakes 98

Data Lakes Data Warehouse Data Quality Data Models 98

AWS Machine Learning Blog

AUGUST 4, 2023

In the data flow view, you can now see a new node added to the visual graph. For more information on how you can use SageMaker Data Wrangler to create Data Quality and Insights Reports, refer to Get Insights On Data and Data Quality. SageMaker Data Wrangler offers over 300 built-in transformations.

ML 88

ML ML AWS AI 88

phData

MAY 6, 2024

Data Quality Next, dive into the details of your data. 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.

Data Lakes 52

Data Lakes Data Warehouse SQL Cloud Data 52

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

ETL 59

ETL Data Pipeline ML ML 59

phData

SEPTEMBER 27, 2024

Data Quality Management : Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. Here’s where it gets really interesting.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

phData

NOVEMBER 1, 2024

It serves as a vital protective measure, ensuring proper data access while managing risks like data breaches and unauthorized use. Strong data governance also lays the foundation for better model performance, cost efficiency, and improved data quality, which directly contributes to regulatory compliance and more secure AI systems.

Data Governance 52

Data Governance Data Quality ML ML 52