Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. This data ingestion process can be accomplished by either querying the source directly, using upstream systems to publish events, or some combination of the two.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source. Data observability in the data lakehouse Data lakehouse architecture is a prominent innovation in data management that continues to evolve.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. This starts at the data source. Data observability in the data lakehouse Data lakehouse architecture is a prominent innovation in data management that continues to evolve.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. Better data observability equals better data quality.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Ascend.io

JUNE 8, 2023

Is it possible to treat data not just as a necessary operational output, but as a product that holds immense strategic value? Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Maxime Beauchemin

JANUARY 20, 2017

I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. Instead, Facebook came to realize that the work we were doing transcended classic business intelligence. I wasn’t promoted or assigned to this new role.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67