Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. How Did the Modern Data Stack Get Started? Reverse ETL tools.

Data Warehouse 52

Data Warehouse ETL Cloud Data Tableau 52

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 92

Tableau Data Lakes Data Warehouse SQL 92

Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

Big Data 104

Big Data Big Data Data Engineering Data Engineer 104

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Precisely

MAY 13, 2024

As enterprise technology landscapes grow more complex, the role of data integration is more critical than ever before. Wide support for enterprise-grade sources and targets Large organizations with complex IT landscapes must have the capability to easily connect to a wide variety of data sources.

Data Governance 52

Data Governance Data Pipeline Data Quality Cloud Data 52

Precisely

APRIL 3, 2023

It simply wasn’t practical to adopt an approach in which all of an organization’s data would be made available in one central location, for all-purpose business analytics. To speed analytics, data scientists implemented pre-processing functions to aggregate, sort, and manage the most important elements of the data.

ETL 52

ETL Database Analytics Analytics 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

Data Governance 59

Data Governance ML ML Cloud Data 59