Data Engineering Podcast

FEBRUARY 20, 2022

In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. RudderStack’s smart customer data pipeline is warehouse-first. RudderStack’s smart customer data pipeline is warehouse-first.

Systems 100

Systems Software Engineer Software Engineering Data Warehouse 100

Data Engineering Podcast

JANUARY 1, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. Missing data?

Data Warehouse 100

Data Warehouse Data Lake SQL Hadoop 100

Data Engineering Podcast

OCTOBER 22, 2021

In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.

Engineering 100

Engineering Business Intelligence BI Data Warehouse 100

Christophe Blefari

SEPTEMBER 28, 2023

We need to store, process and visualise data, everything else is just marketing. I often say that data engineering is boring, insanely boring. When you are a data engineer you're getting paid to build systems that people can rely on. For downstream data quality there are also a lot of tools.

Cloud Storage 147

Cloud Storage Big Data Hadoop SQL 147

Data Engineering Podcast

MAY 3, 2021

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

JULY 3, 2022

Summary The perennial challenge of data engineers is ensuring that information is integrated reliably. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. Data teams are increasingly under pressure to deliver.

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Data Engineering Podcast

OCTOBER 15, 2021

In this episode they discuss the recent work that has been done by the community, how their work is building on top of that foundation, and how you can get started with DataHub for your own work to manage data discovery today. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

Metadata 100

Metadata BI Data Warehouse Government 100

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Data Engineering Podcast

JULY 17, 2022

In this episode Crux CTO Mark Etherington discusses the different costs involved in managing external data, how to think about the total return on investment for your data, and how the Crux platform is architected to reduce the toil involved in managing third party data. Tired of deploying bad data?

Data Management 130

Data Management Management Metadata MongoDB 130

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. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.

Architecture 130

Architecture Technology Google Cloud Metadata 130

Workfall

JUNE 12, 2023

In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data. Data, the lifeblood of organisations, holds the key to unlocking untapped potential and propelling businesses forward. Where Is CDC Used and Who Uses It?

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Podcast

SEPTEMBER 4, 2022

In this episode Gopal Erinjippurath discusses the data engineering challenges of building and serving those data sets, and how they are distilling complex climate information into consumable facts so you don’t have to be an expert to understand it. Go to dataengineeringpodcast.com/ascend and sign up for a free trial.

MongoDB 100

MongoDB Scala MySQL Data Ingestion 100

Data Engineering Podcast

JULY 31, 2022

Summary Data lineage is the roadmap for your data platform, providing visibility into all of the dependencies for any report, machine learning model, or data warehouse table that you are working with. For more information on Prefect, visit dataengineeringpodcast.com/prefect.

IT 100

IT Metadata MongoDB MySQL 100

Ascend.io

DECEMBER 19, 2023

You’re extracting and loading data first, then transforming it in Snowflake’s cloud data warehouse. These features make Snowflake a great solution for data engineering, but the question arises: why might you still need a separate ETL tool? that you can combine to create custom data workflows.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. But a growing company means growing data needs. Image courtesy of Prefect.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Visit: dataengineeringpodcast.com/data-council today. Your first 30 days are free!

Data Lake 262

Data Lake High Quality Data Data Warehouse Google Cloud 262

Monte Carlo

DECEMBER 13, 2023

“Schema change monitors are critical because we aren’t always informed by the engineering team about schema changes in the source database,” Daniel says. Thanks to Monte Carlo, we can detect these issues very quickly — most of the time, even before our data workflow fails, so users aren’t impacted.

Data Ingestion 52

Data Ingestion BI SQL Data Warehouse 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

Data Engineering Podcast

JANUARY 1, 2022

In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. star/snowflake schema, data vault, etc.)

Data Warehouse 100

Data Warehouse BI Data Workflow Coding 100

DataKitchen

JANUARY 3, 2022

If the IT or data engineering team can’t respond with an enabling data platform in the required time frame, the business analyst does the necessary data work themselves. This ad hoc data engineering work often means coping with numerous data tables and diverse data sets using Alteryx, SQL, Excel or similar tools. .

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Data Engineering Podcast

FEBRUARY 4, 2024

In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex.

SQL 173

SQL Data Lake High Quality Data Data Pipeline 173

Data Engineering Weekly

JANUARY 15, 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. Take control of your customer data today.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Meltano

OCTOBER 5, 2022

Interested in becoming a data engineer? The need for data experts in the U.S. job market is expected to grow by 22% in this decade, and according to LinkedIn’s 2020 report , a data engineer is listed as the 8th fastest growing job today. But what is data engineering exactly and what does a data engineer do?

Data Engineering 52

Data Engineering Data Engineer Engineering Raw Data 52

Data Engineering Podcast

JANUARY 21, 2024

In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP).

Data Lake 147

Data Lake High Quality Data NoSQL Data Warehouse 147

Data Engineering Podcast

APRIL 3, 2022

Summary The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. In this episode Satish Jayanthi explains how he is building a framework to allow enterprises to move quickly while maintaining guardrails for data workflows.

Data Warehouse 100

Data Warehouse Data Workflow Data Architecture SQL 100

Data Engineering Podcast

DECEMBER 18, 2022

In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows. Or sent out frustrating slack messages trying to find the right data set?

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Databand.ai

AUGUST 30, 2023

Here are some of the reasons why DataOps tools are important: Improved Collaboration DataOps tools enable better collaboration between data teams, including data engineers, data scientists, and data analysts.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Data Engineering Podcast

NOVEMBER 14, 2021

In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Data Warehouse 100

Data Warehouse BI Data Workflow Data 100

Data Engineering Podcast

OCTOBER 29, 2021

In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity schema" that is powering the Narrator platform, and how it has allowed his customers to perform self-service exploration of their business domains without being blocked by schema evolution in the data warehouse.

Data Warehouse 100

Data Warehouse BI Data Workflow Data Pipeline 100

Data Engineering Podcast

NOVEMBER 27, 2021

If you are having trouble answering questions for your business with the data that you generate and collect internally, then it is definitely worthwhile to explore the information available from external sources. Missing data? Start trusting your data with Monte Carlo today! Struggling with broken pipelines? Stale dashboards?

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Data Engineering Podcast

JUNE 1, 2021

This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create. RudderStack’s smart customer data pipeline is warehouse-first. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt.

Data Pipeline 100

Data Pipeline Data Warehouse Data Workflow Data 100

Monte Carlo

APRIL 28, 2023

Data Engineers : Data engineers who work with data pipelines and ETL processes use DuckDB to enhance the performance of their data processing tasks. They can leverage DuckDB’s efficient query execution and integration with data formats like Parquet to optimize their data workflows.

Data Warehouse 52

Data Warehouse Programming Language Datasets Data Pipeline 52

Monte Carlo

JUNE 14, 2023

For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 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. Data Transformation Because of the many variations of source systems, the data collected during the ingestion phase is often raw, messy, and unstructured.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Monte Carlo

MAY 25, 2023

However, once data platforms scale beyond a certain point, managing jobs will quickly become unwieldy by in-house standards. Data orchestration supports the data engineering lifecycle by bringing automation to data pipelines. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52