Monte Carlo

MARCH 14, 2023

As part of my job, I’m fortunate enough to speak with data leaders far and wide about how they are tackling some of our industry’s biggest challenges, from implementing data mesh to scaling self-service analytics. The data team even has a mission statement: to deliver dependable data and algorithms to the company.

Transportation 40

Transportation Government Google Cloud Architecture 40

Monte Carlo

APRIL 16, 2022

The data warehouse is the foundation of the modern data stack, so it caught our attention when we saw Convoy head of data Chad Sanderson declare, “ the data warehouse is broken ” on LinkedIn. I’ll let you decide for yourself if the “immutable data warehouse” (or active vs passive ETL) is the right path for your data team.

Data Warehouse 52

Data Warehouse Architecture Data Government 52

Knowledge Hut

MARCH 28, 2024

In this article, we are diving into a variety of user research methods and best practices, accompanied by expert insights to translate your research data into actionable improvements for the app. Cost Savings: Identifying issues early in development reduces the need for expensive redesigns, saving time and resources.

Recruitment 52

Recruitment Media Data Collection Designing 52

Netflix Tech

MARCH 14, 2023

Earlier we shared the details of one of these algorithms , introduced how our platform team is evolving the media-specific machine learning ecosystem , and discussed how data from these algorithms gets stored in our annotation service. A key opportunity is to automate these mundane tasks. There are two components to this experience.

Media 116

Media Building Algorithm Machine Learning 116

Monte Carlo

APRIL 25, 2023

Struggling with data quality? When it comes to making your data more reliable at scale, there are a few routes you can take: (1) test your most critical pipelines and hope for the best, (2) automated data quality monitoring, (3) end-to-end data observability. Table of Contents What is data testing?

Data Pipeline 52

Data Pipeline Data Metadata Algorithm 52

Monte Carlo

APRIL 24, 2023

Struggling with data quality? When it comes to making your data more reliable at scale, there are a few routes you can take: (1) test your most critical pipelines and hope for the best, (2) automated data quality monitoring, (3) end-to-end data observability. Table of Contents What is data testing?

Data Pipeline 52

Data Pipeline Data Metadata Algorithm 52

Picnic Engineering

JULY 26, 2022

Data Mesh, or Hub-and-Spoke? and other reflections on building data governance. What are your thoughts on the topic of Data Mesh, and whether to adopt it at Picnic? As a Data Lead, I feel that I don’t have the best seat at the organizational table. What exactly is a “lakeless” Data Warehouse? Any advice?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

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? John Steinmetz, VP of Data & Analytics at shiftkey, echoes this sentiment.

Software Engineer 52

Software Engineer Software Engineering Retail Data 52

Monte Carlo

JUNE 17, 2021

As more companies rely on more data to drive their product development and strategic decision making, it’s never been more important for this data to be trusted and accurate. Data downtime , defined as periods of time when data is missing, inaccurate, or otherwise erroneous, is a painfully familiar reality for data teams.

Data Pipeline 40

Data Pipeline Software Engineer Software Engineering Data Lake 40

Ascend.io

MAY 19, 2023

Picture a data engineering leader, like a captain at the helm of a ship, navigating the vast ocean of data. The modern data stack promised a vessel with cutting-edge navigation tools to sail smoothly through this ocean. Enter the post-modern data stack. This is the modern data stack as we know it today.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Zalando Engineering

SEPTEMBER 28, 2022

Problem Space: The need for a flexible content solution The Zalando Fashion Store is first and foremost a platform to help our customers find the products they want, and it employs various strategies to personalise the experience for each customer. However, it had many limitations affecting scalability.

Certification 52

Certification Architecture Data Schemas Metadata 52

Cloudera

OCTOBER 6, 2020

I know of companies that have been perpetually “doing cloud” for 10 years, but very few that have “done cloud” in a way that democratises and makes data accessible, with minimal pain points. But getting the best results requires a comprehensive strategy that supports the entire data and analytics lifecycle (i.e.,

Bytes 65

Bytes Education Cloud Data 65

Zalando Engineering

OCTOBER 14, 2021

You’ll find the previous chapters here and here. The success of that last team brought with it many requests for collaboration, which had to be balanced with SRE’s own roadmap. This is a clear investment from the company into the value SRE repeatedly demonstrated. Thanks for following our story. Thanks for following our story.

Programming Language 52

Programming Language Engineering Project Education 52

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

Knowledge Hut

MAY 6, 2024

With varied perceptions of the underlying problems, assumptions over customer behavior and cognitive bias for a solution, it may get cumbersome to determine the right product that delivers value. In this article, we will discover ways that can help teams determine the key objectives and develop a common vision for the product.

Media 98

Media Process Building Project 98

ProjectPro

NOVEMBER 11, 2014

Confused over which framework to choose for big data processing - Hadoop MapReduce vs. Apache Spark. This blog helps you understand the critical differences between two popular big data frameworks. Hadoop and Spark are popular apache projects in the big data ecosystem. It allows you to process just a batch of stored data.

Hadoop 40

Hadoop Scala Machine Learning 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. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits.

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. To help clarify, we reviewed hundreds of data observability deployments to identify 61 real data observability use cases and benefits.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52