Monte Carlo

SEPTEMBER 6, 2022

Say it with me: bad data is inevitable. It doesn’t care about how proactive you are at writing dbt tests, how perfectly your data is modeled, or how robust your architecture is. The possibility of a major data incident (Null value? Errant schema change? Failed model?) Check it out. Here’s why.

Software Engineer 52

Software Engineer Software Engineering Retail Data 52

dbt Developer Hub

NOVEMBER 3, 2022

Let’s discuss how to convert events from an event-driven microservice architecture into relational tables in a warehouse like Snowflake. One key focus of our platform is data quality. In this blog post we’ll dive into how we tackled one source of quality issues: directly relying on upstream database schemas.

Kafka 52

Kafka ETL Tools BI Database 52

Cloudera

SEPTEMBER 17, 2020

For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. CDP data lifecycle integration and SDX security and governance. Enterprise Data Engineering From the Ground Up.

Data Pipeline 104

Data Pipeline Data Engineering Data Engineer Engineering 104

ProjectPro

OCTOBER 20, 2021

With so many pseudo-data scientists cropping up due to numerous data science bootcamps and courses that offer theoretical learning, the interview questions for AI and machine learning jobs are getting streamlined to filter those who understand how real-world implementation works. Gartner is a market leader in market research.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

ProjectPro

APRIL 4, 2015

IT organizations from various domains are investing in big data technologies, increasing the demand for technically competent Hadoop developers. To build career as a Hadoop developer, one must be clear with Hadoop concepts and have a working knowledge of analysing data using MapReduce, Hive and Pig.

Hadoop 40

Hadoop Big Data Recruitment Java 40

Monte Carlo

MAY 17, 2023

Data observability, an organization’s ability to fully understand the health and quality of the data in their systems, has become one of the hottest technologies in modern data engineering. Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data observability, an organization’s ability to fully understand the health and quality of the data in their systems, has become one of the hottest technologies in modern data engineering. Because the technology is so extensible, there have been a wide array of suggestions–some more grounded than others–for how it can be used.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52