Precisely

JULY 18, 2024

Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata. As data grows exponentially, so do the complexities of managing and leveraging it to fuel AI and analytics.

Data Management 52

Data Management Management Metadata Database-centric 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Metadata layer 4.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. Metadata layer 4.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

AUGUST 30, 2024

It’s our goal at Monte Carlo to provide data observability and quality across the enterprise by monitoring every system vital in the delivery of data from source to consumption. We started with popular modern data warehouses and quickly expanded our support as data lakes became data lakehouses.

SQL 40

SQL Engineering Data Lake High Quality Data 40

Wayne Yaddow

FEBRUARY 18, 2025

Carefully curated test data (realistic samples, edge cases, golden datasets) that reveal issuesearly. Proper tooling & environment (Python ecosystem for Great Expectations, data warehouse credentials and macros fordbt).

Data Pipeline 52

Data Pipeline SQL Raw Data Python 52

Monte Carlo

AUGUST 10, 2023

Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it. Both terms are focused on the practice of ensuring healthy, high quality data across an organization. It is still relevant today.

Data Pipeline 98

Data Pipeline Software Engineering Software Engineer Machine Learning 98

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

APRIL 21, 2022

Adopting a cloud data warehouse like Snowflake is an important investment for any organization that wants to get the most value out of their data. This query will fetch a list of all tables within a database, along with helpful metadata about their settings.

Metadata 52

Metadata Bytes Government Data 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69

Monte Carlo

AUGUST 13, 2024

In addition to discussing what the data observability category is, Gartner also makes a point to explain how it’s both different and complements traditional data quality approaches, as well as the vendors placed in their augmented data quality category that have their own critical capabilities—some of which overlap.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 52

Monte Carlo

FEBRUARY 8, 2024

How does it impact data warehouse/lakehouse performance? Data observability tools should offer both broad automated metadata monitoring across all the tables once they have been added to your selected schemas, as well as deep monitoring for issues inherent in the data itself. Robust role based access controls?

BI 59

BI Data Warehouse Data Pipeline Data 59

Monte Carlo

JUNE 23, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. We imagined what it would be like to manage big data quality at that scale.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Ascend.io

JUNE 8, 2023

Whether it be the marketing team seeking customer insights, the finance team working on budgeting, or executives crafting business strategies, data needs to be shared in a manner that aligns with their specific objectives and competencies. It is the stage where data truly becomes a product, delivering tangible value to its end users.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Monte Carlo

AUGUST 30, 2023

Often, teams run custom data tests as part of a deployment pipeline, or scheduled on production systems via job schedulers like Apache Airflow, dbt Cloud, or via in-built schedulers in your data warehouse solution. We could talk data quality all day long. Here are some common use cases for dbt tests.

SQL 52

SQL Datasets Database High Quality Data 52

Monte Carlo

AUGUST 17, 2021

“What excites me about Monte Carlo is their vision for making the delivery of data more reliable and transparent through observability. Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. “We Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. “We Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52