Data Engineering Weekly

APRIL 16, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make collecting data from every application, website, and SaaS platform easy, then activating it in your warehouse and business tools. 🍒 We'll send exclusive The Data Stack Show swag just for participating!

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Ascend.io

SEPTEMBER 14, 2023

The rise of data-intensive operations has positioned data engineering at the core of today’s organizations. As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand. Why Python for Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Knowledge Hut

FEBRUARY 27, 2023

A data engineer is a key member of an enterprise data analytics team and is responsible for handling, leading, optimizing, evaluating, and monitoring the acquisition, storage, and distribution of data across the enterprise. Data Engineers indulge in the whole data process, from data management to analysis.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Grouparoo

OCTOBER 6, 2021

It was a fun experience and I think we made a good choice by picking 97 Things Every Data Engineer Should Know. This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

DataKitchen

FEBRUARY 21, 2023

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Data team morale is consistent with DataKitchen’s own research. We surveyed 600 data engineers , including 100 managers, to understand how they are faring and feeling about the work that they are doing.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Cloudera

DECEMBER 13, 2023

To enable LGIM to better utilize its wealth of data, LGIM required a centralized platform that made internal data discovery easy for all teams and could securely integrate external partners and third-party outsourced data pipelines.

Pipeline-centric 88

Pipeline-centric Professional Services Datasets BI 88

Data Engineering Podcast

AUGUST 13, 2022

Summary Data engineers have typically left the process of data labeling to data scientists or other roles because of its nature as a manual and process heavy undertaking, focusing instead on building automation and repeatable systems. Data stacks are becoming more and more complex.

Machine Learning 100

Machine Learning Pipeline-centric Database-centric MongoDB 100

Ascend.io

OCTOBER 20, 2023

The Nuances of Snowflake Costing Snowflake’s pricing strategy is an exemplification of its user-centric approach: pay for what you use. The Predictability of Pipelines In stark contrast to ad-hoc queries, pipelines are where cost optimization efforts can yield significant dividends.

Pipeline-centric 52

Pipeline-centric IT Data Pipeline Bytes 52

Christophe Blefari

APRIL 8, 2023

I still firmly believe that this is not the role of a data engineer. A data engineer should still be a software engineer working with data, empowering others with tooling and apps. Data modeling should not be a required data engineer skill. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

Christophe Blefari

APRIL 8, 2023

I still firmly believe that this is not the role of a data engineer. A data engineer should still be a software engineer working with data, empowering others with tooling and apps. Data modeling should not be a required data engineer skill. Enters the analytics engineer.

Pipeline-centric 130

Pipeline-centric Database-centric Algorithm Data 130

Snowflake

JUNE 28, 2023

At Snowflake, we’re helping data scientists, data engineers, and application developers build faster and more efficiently in the Data Cloud. The end-to-end process requires collaboration across many data, engineering and business teams in order to capitalize on the value that can be captured from AI-powered insights.

Pipeline-centric 54

Pipeline-centric Programming Language Python Government 54

Ascend.io

JUNE 21, 2023

Cost Savings : Insights derived from data products can directly lead to cost savings in the business. Risk Reduction : Data-centric decision-making can also contribute to risk reduction. In the case of data products, these are networks of data pipelines, which makes them an integral part of your modern operational machinery.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

Pipeline Data Engineering

MARCH 2, 2022

Data engineering salon. News and interesting reads about the world of data. The Unbundling of Airflow Gorkem Yurtseven, Co-Founder, Features and Labels A diverse set of tools is unbundling Airflow and this diversity is causing substantial fragmentation in modern data stack. What the hell happened?

Pipeline-centric 52

Pipeline-centric BI Consulting Data Warehouse 52

François Nguyen

MARCH 22, 2021

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. The next problem will be the diversity of these mini data platforms (because of the configuration) and you even go deeper in problems with managing different technologies or version.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Databand.ai

JULY 19, 2023

This helps to enhance data quality, facilitate data governance, and enable regulatory compliance. Notable Data Lineage Tools and Solutions Let’s take a look at several notable data lineage tools that can improve the quality and efficiency of your data pipeline.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

DataKitchen

AUGUST 27, 2021

It implemented hundreds of schema and data set changes per week without introducing errors. Arguably the most agile and effective data analytics capability in the pharmaceutical industry was accomplished cost-effectively, with a data engineering team of seven and another 10-12 data analysts.

Pharmaceutical 98

Pharmaceutical Pipeline-centric Data Lake Data Analytics 98

DataKitchen

NOVEMBER 4, 2021

It often takes months to progress from a data lake to the final delivery of insights. One data engineer called it the “last mile problem.” . In our many conversations about data analytics, data engineers, analysts and scientists have verbalized the difficulty of creating analytics in the modern enterprise.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Monte Carlo

AUGUST 2, 2022

As I begin to plot our data mesh implementation plan at Sanne Group, a leading asset management services provider, I can see why this idea that was originally introduced in 2019 has gained so much mindshare, but so few fully formed meshes nearly four years later. They will be staffed accordingly.

Pipeline-centric 52

Pipeline-centric Government Finance Data 52

Knowledge Hut

JULY 3, 2023

This process consists of feeding data from various sources and building it to be available for analysis, storage, or next processing. To achieve this goal, pursuing Data Engineer certification can be highly beneficial. What is Real-Time Data Ingestion? Why is Real-Time Data Ingestion Important?

Data Ingestion 52

Data Ingestion Pipeline-centric Google Cloud Media 52

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is Data Science? What are the roles and responsibilities of a Data Engineer? And many more.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

ProjectPro

DECEMBER 16, 2021

They need to know everything about the data and apply various mathematical and statistical tools to identify the most significant features using feature selection, feature engineering , feature transformation, etc. On the other hand, Data Scientists work in coordination with data analysts and data engineers.

Machine Learning 52

Machine Learning Engineering Pipeline-centric Database-centric 52

Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Pipeline 101

Data Pipeline Data Engineering Data Engineer Engineering 101

ProjectPro

JUNE 1, 2017

Datos IO has extended its on-premise and public cloud data protection to RDBMS and Hadoop distributions. RecoverX is described as app-centric and can back up applications data whilst being capable of recovering it at various granularity levels to enhance storage efficiency. now provides hadoop support.

Hadoop 52

Hadoop Medical Pipeline-centric Database-centric 52

Ascend.io

MAY 31, 2023

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Data Engineering Podcast

APRIL 7, 2019

Summary How much time do you spend maintaining your data pipeline? This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems. How does the data-centric approach of DataCoral differ from the way that other platforms think about processing information?

Data Pipeline 100

Data Pipeline Pipeline-centric Database-centric AWS 100

Monte Carlo

MARCH 28, 2024

As well as answering that question, we’ll be delving into how they differ from data scientists and data engineers. We’ll also be covering what they do day to day, what sort of salaries they command, and how individuals might go about pivoting to become a DataOps engineer. What does a DataOps engineer do?

Pipeline-centric 75

Pipeline-centric Engineering BI Google Cloud 75

Ascend.io

AUGUST 29, 2023

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

DareData

JULY 29, 2020

One paper suggests that there is a need for a re-orientation of the healthcare industry to be more "patient-centric". Furthermore, clean and accessible data, along with data driven automations, can assist medical professionals in taking this patient-centric approach by freeing them from some time-consuming processes.

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Data Engineering Podcast

MAY 7, 2023

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management RudderStack helps you build a customer data platform on your warehouse or data lake. However, that's also something we're re-thinking with our warehouse-centric strategy.

Pipeline-centric 130

Pipeline-centric Data Lake Machine Learning Data Warehouse 130

Data Engineering Weekly

JANUARY 8, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Pipelines for data in motion can quickly turn into DAG hell.

Data Engineering 40

Data Engineering Data Engineer Engineering Pipeline-centric 40

Ascend.io

JUNE 8, 2023

Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Dataquest

JANUARY 25, 2017

Data scientists have become extremely sought after , and for good reason — a skilled data scientist can add incredible value to a business. But what about data engineers? A data scientist is only as good as the data they have access to. A data engineer transforms data into a useful format for analysis.

Data Engineering 52

Data Engineering Data Engineer Pipeline-centric Database-centric 52

Snowflake

JUNE 28, 2023

Since its launch two years ago, Snowpark has been empowering data scientists, data engineers, and application developers to streamline their architectures, accelerate development, and boost the performance of data engineering and ML/AI workloads on Snowflake.

Python 52

Python Accessible Accessibility Pipeline-centric 52

Towards Data Science

MAY 19, 2023

Business users are unable to find and access data assets critical to their workflows. Data engineers spend countless hours troubleshooting broken pipelines. The data team is constantly burning out and has a high employee turnover. Stakeholders fail to see the ROI behind expensive data initiatives.

Pipeline-centric 105

Pipeline-centric Data Software Engineer Software Engineering 105

Knowledge Hut

SEPTEMBER 26, 2023

Key Features of Azure Synapse Here are some of the key features of Azure Synapse: Cloud Data Service: Azure Synapse operates as a cloud-native service, residing within the Microsoft Azure cloud ecosystem. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Monte Carlo

NOVEMBER 14, 2023

This is the world that data orchestration tools aim to create. Data orchestration tools minimize manual intervention by automating the movement of data within data pipelines. According to one Redditor on r/dataengineering, “Seems like 99/100 data engineering jobs mention Airflow.”

Pipeline-centric 40

Pipeline-centric Google Cloud Data Workflow Python 40

Monte Carlo

JULY 28, 2022

But perhaps one of the most common reasons for data quality challenges are software feature updates and other changes made upstream by software engineers. These are particularly frustrating, because while they are breaking data pipelines constantly, it’s not their fault. He suggested : “Private vs. public methods.

Pipeline-centric 52

Pipeline-centric Software Engineer Software Engineering Database-centric 52