Monte Carlo

FEBRUARY 20, 2024

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

Databand.ai

JUNE 28, 2023

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Data Engineering Podcast

NOVEMBER 20, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

Data Lake 130

Data Lake Data Ingestion MongoDB Scala 130

Data Engineering Podcast

OCTOBER 30, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

Engineering 100

Engineering MongoDB Scala MySQL 100

Data Engineering Podcast

NOVEMBER 6, 2022

Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

MongoDB 130

MongoDB Scala MySQL Data Lake 130

Data Engineering Podcast

NOVEMBER 13, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.

MongoDB 100

MongoDB Scala Google Cloud MySQL 100

Monte Carlo

MAY 26, 2023

Pro-tip : Don’t confuse data freshness with data latency. Data latency is the time between when the event occurs and when the data is available in the core data system (like a data warehouse) whereas data freshness is how recently the data within the final asset (table, BI report) has been updated.

Data Pipeline 98

Data Pipeline Data Data Warehouse Machine Learning 98