Monte Carlo

JANUARY 3, 2024

Many data teams make the mistake of excluding key players from their Gen AI tiger teams, and that’s costing them in the long run. Software engineers to develop the code, the user facing application and the API calls. Data scientists to consider new use cases, fine tune your models , and push the team in new directions.

Technology 113

Technology Database Data Governance Data Engineering 113

Monte Carlo

AUGUST 10, 2023

Observability , a more recent addition to the software engineering lexicon, speaks to this need, and refers to the monitoring, tracking, and triaging of incidents to prevent software application downtime. (For Both terms are focused on the practice of ensuring healthy, high quality data across an organization.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Monte Carlo

AUGUST 31, 2023

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. As data becomes not just an output but a financial commodity for many organizations, it’s important that this information can be trusted.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

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 Scala Data Warehouse 52

Data Engineering Weekly

NOVEMBER 13, 2022

I'm a software engineer; how can I transition to a Machine Learning engineer? link] Meta: Tulip - Schematizing Meta’s data platform Numerous heterogeneous services make up a data platform, such as warehouse data storage and various real-time systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Maxime Beauchemin

JANUARY 20, 2017

They’re highly analytical, and are interested in data visualization. Unlike data scientists — and inspired by our more mature parent, software engineering  — data engineers build tools, infrastructure, frameworks, and services. The data engineer’s focal point is the data warehouse and gravitates around it.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Towards Data Science

MARCH 29, 2023

Great reads on modeling, processes, and leadership Photo by Emil Widlund on Unsplash At the very start of my journey in data, I thought I was going to be a data scientist, and my first foray into data was centered on studying statistics and linear algebra, not software engineering or database management.

Data Warehouse 68

Data Warehouse BI Healthcare Database 68

Monte Carlo

JANUARY 25, 2023

There is, however, an added dimension to this relationship: data producers are often consumers of upstream data sources. Data warehouse producers wear both hats working with upstream producers so they can consume high-quality data and producing high-quality data to provide to their consumers.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Lyft Engineering

OCTOBER 3, 2023

High-quality data is necessary for the success of every data-driven company. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale.

Big Data 84

Big Data Metadata Data Warehouse Data 84

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Monte Carlo

APRIL 26, 2022

DataOps is a discipline that merges data engineering and data science teams to support an organization’s data needs, in a similar way to how DevOps helped scale software engineering. What is DataOps?

IT 64

IT Data Pipeline Data Engineering Data Engineer 64

Netflix Tech

APRIL 26, 2022

ML algorithms can be only as good as the data that we provide to it. This post will focus on the large volume of high-quality data stored in Axion?—?our While “ keep the design simple ” is a frequently shared learning in software engineering, it is not always easy to achieve.

Metadata 75

Metadata Datasets Machine Learning Designing 75

ProjectPro

FEBRUARY 21, 2023

Google Cloud Certified Professional Data Engineer Certifications An individual is fit for taking the GCP Data Engineering certification exam if he/she- Has more than three years of prior data engineering experience, including at least one year of solution design and management using Google Cloud.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

MAY 17, 2023

Luckily, the data observability solution caught what otherwise would have been an otherwise difficult to detect issue. Mitigate Risk of Data Failures Software engineers are also challenged by system and code issues, but data engineers are faced with the unique challenge of issues within the data itself.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Luckily, the data observability solution caught what otherwise would have been an otherwise difficult to detect issue. Mitigate Risk of Data Failures Software engineers are also challenged by system and code issues, but data engineers are faced with the unique challenge of issues within the data itself.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52