DataKitchen

JANUARY 3, 2022

They need high-quality data in an answer-ready format to address many scenarios with minimal keyboarding. What they are getting from IT and other data sources is, in reality, poor-quality data in a format that requires manual customization. DataOps Process Hub.

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Monte Carlo

AUGUST 13, 2024

This year data observability skyrocketed to the top of the Gartner’s Hype Cycles. According to Gartner, 50% of enterprise companies implementing distributed data architectures will have adopted data observability tools by 2026 – up from just ~20% in 2024. Image courtesy of Gartner.

Data 52

Data Data Warehouse Data Pipeline Data Architecture 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

Monte Carlo

JANUARY 31, 2023

If you actually try to look at [schema changes or anomalies] manually in your entire data warehouse, that’s what takes so much effort to actually capture. Monte Carlo helped the Checkout.com data team combat those challenges. “ML-based anomaly detection beats manual threshold basically any day of the week,” says Martynas. “If

Data 52

Data Data Engineering Data Engineer High Quality Data 52

Monte Carlo

MARCH 28, 2024

For example, when you think about a data warehouse , it’s really just a codebase—primarily composed of SQL—that’s serving internal customers like other analysts, data scientists, and product managers who are using that data to go and make business decisions. If data is your product, then it needs to be high quality.

Data 52

Data High Quality Data Machine Learning Accessible 52

Databand.ai

JANUARY 24, 2023

Supporting all of this requires a modern infrastructure and data architecture with appropriate governance. DataOps helps ensure organizations make decisions based on sound data. Enter DataOps. In doing so, it allows teams to identify problems faster and, therefore, deliver solutions faster. Who’s Involved in a DataOps Team?

Retail 52

Retail Banking Government Manufacturing 52

Monte Carlo

AUGUST 17, 2021

Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers. While a lot has changed in five years, one thing has always remained the same: the company’s commitment to building an insights-driven culture based on accurate and reliable data.

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