Data Engineering Podcast

NOVEMBER 26, 2023

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Data Engineering Podcast

DECEMBER 18, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Struggling with broken pipelines? Missing data? Atlan is the metadata hub for your data ecosystem.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

DECEMBER 29, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Struggling with broken pipelines? Missing data? Atlan is the metadata hub for your data ecosystem.

Management 130

Management Metadata Business Intelligence Data Lake 130

Data Engineering Podcast

JUNE 17, 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. What are the most interesting, innovative, or unexpected ways that you have seen column-aware data modeling used?

Data Lake 130

Data Lake Metadata Data Architecture Machine Learning 130

Monte Carlo

MAY 31, 2022

When a data pipeline breaks, data engineers need to immediately understand where the rupture occurred and what has been impacted. Data downtime is costly. Field-level data lineage (not necessarily Spark lineage) with hundreds of connections between objects in upstream and downstream tables.

Data Lake 52

Data Lake Building Scala Metadata 52

Monte Carlo

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 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. 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. What you have to code is this workflow !

Architecture 130

Architecture Technology Google Cloud Metadata 130

Ascend.io

APRIL 22, 2024

This initiative is more than just an upgrade; it’s a reimagining of what a Data Automation Platform can be: dynamic, extensible, and highly intelligent. A unified platform that combines a powerful metadata core, an extensible plugin architecture, DataAware automation, and multiple AI Assistants.

Project 52

Project Metadata Data Pipeline Data Engineering 52

Data Engineering Podcast

NOVEMBER 27, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Struggling with broken pipelines? Missing data? Stale dashboards? If this resonates with you, you’re not alone.

Data Process 100

Data Process Process Metadata Business Intelligence 100

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Data Engineering Podcast

MAY 22, 2022

Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. Stop struggling to speed up your data lake.

Machine Learning 130

Machine Learning Data Engineering Data Engineer Cloud 130

Christophe Blefari

JANUARY 20, 2024

Then here a list of global resources that can help you navigate through the field: The Data Engineer Roadmap — An image with advices and technology names to watch. Reddit r/dataengineering wiki a place where some data eng definitions are written. You'll be also asked to put in place a data infrastructure.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Data Engineering Podcast

DECEMBER 25, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Struggling with broken pipelines? Missing data? Again, be prepared to have metadata challenges especially.

Building 100

Building Metadata Business Intelligence Data Lake 100

Data Engineering Podcast

DECEMBER 4, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day.

Business Intelligence 100

Business Intelligence Metadata BI MongoDB 100

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Let’s take a closer look.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Towards Data Science

FEBRUARY 9, 2024

As a result, here’s a short list of what inspired me to write an amendment to my original 2021 article : Scale Companies, big and small, are starting to reach levels of data scale previously reserved for Netflix, Uber, Spotify and other giants creating unique services with data.

Building 82

Building Transportation Data Lake Metadata 82

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

Data Engineering Podcast

NOVEMBER 20, 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 130

Data Lake Data Ingestion MongoDB Scala 130

Data Engineering Podcast

AUGUST 21, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or data lake.

Metadata 100

Metadata MongoDB MySQL Data Lake 100

Data Engineering Podcast

OCTOBER 9, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc.,

Metadata 100

Metadata AWS MongoDB MySQL 100

Monte Carlo

MARCH 4, 2024

To do this, data observability solutions like Monte Carlo will piece together information from query logs, data aggregates and system metadata. All of this is to say that how table formats are structured, and specifically the metadata, impacts how they can be monitored and observed.

Metadata 52

Metadata Data Lake Data Warehouse Data Engineering 52

Data Engineering Podcast

AUGUST 28, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

RandomTrees

FEBRUARY 6, 2024

Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Towards Data Science

FEBRUARY 6, 2024

Order snapshots are stored in my own development area (image by the author) To prevent my extractions from impacting performance on the operational side, I queried this data regularly and stored it in a persistent staging area (PSA) within my data warehouse. Metadata update Data products need to be understandable.

Systems 83

Systems Raw Data Metadata Data Cleanse 83

Data Engineering Podcast

SEPTEMBER 4, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or data lake.

IT 100

IT Metadata MongoDB MySQL 100

Data Engineering Podcast

DECEMBER 19, 2021

Summary Building a well managed data ecosystem for your organization requires a holistic view of all of the producers, consumers, and processors of information. The team at Metaphor are building a fully connected metadata layer to provide both technical and social intelligence about your data.

Systems 100

Systems Building Metadata Data Warehouse 100

Monte Carlo

JUNE 26, 2023

Over the past decade, Databricks and Apache Spark™ not only revolutionized how organizations store and process their data, but they also expanded what’s possible for data teams by operationalizing data lakes at an unprecedented scale across nearly infinite use cases. billion in 2020 to $17.6

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Grouparoo

OCTOBER 6, 2021

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. 7 Be Intentional About the Batching Model in Your Data Pipelines Different batching models.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. Anyone can query the metadata any time anywhere to get the information they need.

Metadata 52

Metadata Datasets Data Governance Government 52

Monte Carlo

MAY 18, 2023

We’ve been open about this work, providing very technical deep dives into how we developed column-level lineage and even some of our failures in building Spark data lineage for data lakes. The Spark data lake lineage example is helpful for context here.

Metadata 64

Metadata Data Lake Government Data Governance 64

Ascend.io

MAY 23, 2023

The data engineering world is full of tips and tricks on how to handle specific patterns that recur with every data pipeline. Already in 2016, IBM estimated the cost of bad data to be over three trillion dollars, and that was before the chaos of data lakes emerged and orphaned datasets began to swamp the land.

Datasets 52

Datasets Data Pipeline Metadata Database 52

Data Engineering Podcast

MARCH 22, 2021

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. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask.

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

Monte Carlo

MARCH 12, 2024

Google Cloud’s Dataplex is a data fabric tool that enables organizations to discover, manage, monitor, and govern their data across all of their data systems, including their data lakes, data warehouses, data lakehouses, and data marts. Dataplex works with your metadata.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 52

Monte Carlo

MAY 30, 2023

It’s designed to improve upon the performance and usability challenges of older data storage formats such as Apache Hive and Apache Parquet. Schema changes can still create challenges downstream in the data pipeline and may require data observability or other data monitoring to fully prevent data quality issues.

Metadata 52

Metadata Raw Data Data Lake Data 52