Data Engineering Podcast

FEBRUARY 8, 2021

In this episode Zeeshan Qureshi and Michelle Ark share their experiences using DBT to manage the data warehouse for Shopify. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. What kinds of data sources are you working with?

Data Warehouse 100

Data Warehouse Building BI SQL 100

Data Engineering Podcast

APRIL 19, 2021

Summary Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Pipeline 100

Data Pipeline Machine Learning Data Warehouse Datasets 100

Monte Carlo

FEBRUARY 6, 2023

So, you’re planning a cloud data warehouse migration. But be warned, a warehouse migration isn’t for the faint of heart. As you probably already know if you’re reading this, a data warehouse migration is the process of moving data from one warehouse to another. A worthy quest to be sure.

Data Warehouse 52

Data Warehouse AWS Data Validation Data 52

Grouparoo

OCTOBER 26, 2021

As a result, data has to be moved between the source and destination systems and this is usually done with the aid of data pipelines. What is a Data Pipeline? A data pipeline is a set of processes that enable the movement and transformation of data from different sources to destinations.

Data Pipeline 52

Data Pipeline ETL Tools ETL System Data Warehouse 52

Data Engineering Podcast

OCTOBER 14, 2019

Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. They provide an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

Hevo

FEBRUARY 9, 2023

It helps organizations quickly build and maintain data transformations in their data pipelines. It is specifically designed for data analysts, engineers, and scientists who work with large amounts of data stored in data warehouses and other data storage systems.

Data Pipeline 52

Data Pipeline Building Data Warehouse Data Storage 52

Workfall

AUGUST 14, 2023

Reading Time: 7 minutes In today’s data-driven world, efficient data pipelines have become the backbone of successful organizations. These pipelines ensure that data flows smoothly from various sources to its intended destinations, enabling businesses to make informed decisions and gain valuable insights.

Data Pipeline 62

Data Pipeline Raw Data Data Database 62

Data Engineering Podcast

JANUARY 18, 2021

Summary The data warehouse has become the central component of the modern data stack. This is an interesting conversation about the importance of the data warehouse and how it can be used beyond just internal analytics. How do you keep data up to date between the warehouse and downstream systems?

Data Warehouse 100

Data Warehouse BI Data Data Pipeline 100

Cloudera

JUNE 2, 2021

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.

Data Pipeline 88

Data Pipeline Data Warehouse Machine Learning Data Architect 88

Monte Carlo

JANUARY 25, 2023

In this article, Chad Sanderson , Head of Product, Data Platform , at Convoy and creator of Data Quality Camp , introduces a new application of data contracts: in your data warehouse. In the last couple of posts , I’ve focused on implementing data contracts in production services.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Data Engineering Weekly

JULY 5, 2023

link] Policy Genius: Data Warehouse Testing Strategies for Better Data Quality Data Testing and Data Observability are widely discussed topics in Data Engineering Weekly. Perhaps unit test the pipeline? We will see more tools around the unit test framework for the data pipeline soon.

Data Warehouse 52

Data Warehouse Database Data Pipeline SQL 52

Data Engineering Podcast

DECEMBER 16, 2018

As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed.

Data Pipeline 100

Data Pipeline Data Lake Data Warehouse Python 100

Ascend.io

APRIL 12, 2023

From this research, we developed a framework with a sequence of stages to implement data integrity quickly and measurably via data pipelines. Table of Contents Why does data integrity matter? At every level of a business, individuals must trust the data, so they can confidently make timely decisions. Let’s explore!

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

Christophe Blefari

JANUARY 20, 2024

Data engineering inherits from years of data practices in US big companies. Hadoop initially led the way with Big Data and distributed computing on-premise to finally land on Modern Data Stack — in the cloud — with a data warehouse at the center. My advice on this point is to learn from others.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Simon Späti

MARCH 9, 2021

This post focuses on practical data pipelines with examples from web-scraping real-estates, uploading them to S3 with MinIO, Spark and Delta Lake, adding some Data Science magic with Jupyter Notebooks, ingesting into Data Warehouse Apache Druid, visualising dashboards with Superset and managing everything with Dagster.

Data Engineering 189

Data Engineering Data Engineer Engineering Project 189

Ascend.io

AUGUST 31, 2023

Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Christophe Blefari

APRIL 19, 2024

Fast News ⚡️ Theseus against really big data ( credits ) Principal Engineer — Although staffs and principals have been on the career ladder for a long time, there are very few articles on what it takes to become one of the greats. This article covers the whole ladder and the mix of skills needed to reach the top.

MySQL 130

MySQL Data Datasets SQL 130

Advancing Analytics: Data Engineering

JUNE 21, 2019

Hear me out – back in the on-premises days we had data loading processes that connect directly to our source system databases and perform huge data extract queries as the start of one long, monolithic data pipeline, resulting in our data warehouse. Till next time.

Data Warehouse 52

Data Warehouse Business Intelligence Data Data Validation 52

Start Data Engineering

OCTOBER 12, 2021

Introduction Responsibilities of a data engineer 1. Move data between systems 2. Manage data warehouse 3. Schedule, execute, and monitor data pipelines 4. Serve data to the end-users 5. Data strategy for the company 6.

Data Engineering 130

Data Engineering Data Engineer Engineering Data Warehouse 130

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Striim

JULY 7, 2023

A guide on when real-time data pipelines are the most reliable way to keep production databases and warehouses in sync. Analytics teams aren’t quite so concerned with inserting 20 thousand rows in the span of a second — instead, they want to join, filter, and transform tables to get insights from data.

PostgreSQL 52

PostgreSQL MySQL Data Warehouse Data Pipeline 52

Simon Späti

MARCH 9, 2021

This post focuses on practical data pipelines with examples from web-scraping real-estates, uploading them to S3 with MinIO, Spark and Delta Lake, adding some Data Science magic with Jupyter Notebooks, ingesting into Data Warehouse Apache Druid, visualising dashboards with Superset and managing everything with Dagster.

Data Engineering 130

Data Engineering Data Engineer Engineering Project 130

Data Engineering Podcast

DECEMBER 4, 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. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Lake 100

Data Lake MongoDB MySQL Data Warehouse 100

Data Engineering Podcast

DECEMBER 11, 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. RudderStack helps you build a customer data platform on your warehouse or data lake.

Database 100

Database MySQL Data Lake MongoDB 100

Hevo

MARCH 28, 2023

Synapse vs. other big 4 data warehouses is just like that. Azure Synapse is one of the best data warehouses which helps data engineers to have an entire end-to-end data pipeline in one place. Ronaldo vs. Messi- who is the better footballer? Oh, that’s tough!

Data Warehouse 52

Data Warehouse Data Pipeline Data Data Engineering 52

Monte Carlo

FEBRUARY 12, 2024

To differentiate and expand the usefulness of these models, organizations must augment them with first-party data – typically via a process called RAG (retrieval augmented generation). Today, this first-party data mostly lives in two types of data repositories.

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Christophe Blefari

SEPTEMBER 25, 2023

A guide to the Snowflake results cache — Cache is a critical piece to every data warehouse either for reusing data between runs or between stages in the same run. I'd say that Iceberg (or table formats) are probably one of the technology that will incrementally change for the better the way we write data pipelines.

Data 130

Data Data Warehouse Data Storage Data Pipeline 130

Christophe Blefari

SEPTEMBER 25, 2023

A guide to the Snowflake results cache — Cache is a critical piece to every data warehouse either for reusing data between runs or between stages in the same run. I'd say that Iceberg (or table formats) are probably one of the technology that will incrementally change for the better the way we write data pipelines.

Data 130

Data Data Warehouse Data Storage Data Pipeline 130

ThoughtSpot

OCTOBER 27, 2023

Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases , transform it, and load it into the data warehouse.

Building 70

Building MySQL Data Warehouse SQL 70

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

ThoughtSpot

OCTOBER 27, 2023

Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases , transform it, and load it into the data warehouse.

Building 69

Building MySQL Data Warehouse SQL 69

Ascend.io

APRIL 22, 2024

The Evolution of Data Engineering Over the past decade, we’ve seen tremendous change in the data engineering landscape. As companies scale their data and analytics capabilities, their data pipelines and operations have grown increasingly complex. Most of this data is never collected, and lost forever.

Project 52

Project Metadata Data Pipeline Data Engineering 52

Monte Carlo

FEBRUARY 4, 2021

Once you understand (1) what data you’re working with, (2) how it’s used, and (3) who uses it, you’ll be able to set clear, actionable SLAs. (If If you’re still shaky on what these SLAs might look like, I highly recommend reading this Locally Optimistic series on setting data warehouse SLAs. Brooklyn Data Co.’s

Data Pipeline 52

Data Pipeline Software Engineer Software Engineering Data 52

Knowledge Hut

JUNE 30, 2023

The essential theories, procedures, and equipment for creating trustworthy and effective data systems are covered in this book. It explores subjects including data modeling, data pipelines, data integration, and data quality, offering helpful advice on organizing and implementing reliable data solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52