Data Engineering Podcast

MAY 15, 2022

Summary Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. When is a data lake architecture the wrong choice?

Data Lake 100

Data Lake Building Architecture BI 100

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Hevo

NOVEMBER 14, 2024

Databricks Lakehouse is an open data management architecture which combines the scalability, cost-effectiveness, and flexibility of data lakes with the data management and ACID transactions of data warehouses. Databricks Lakehouse is the best of both worlds of data lakes and data warehouses.

Data Lake 40

Data Lake Data Warehouse Business Intelligence Machine Learning 40

Data Engineering Podcast

MAY 1, 2022

Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. 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 Data Integration Cloud Google Cloud 100

Analytics Vidhya

FEBRUARY 27, 2023

Introduction Enterprises here and now catalyze vast quantities of data, which can be a high-end source of business intelligence and insight when used appropriately. Delta Lake allows businesses to access and break new data down in real time.

Data Lake 215

Data Lake Business Intelligence Designing Accessibility 215

Data Engineering Podcast

FEBRUARY 5, 2023

Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses. Go to [dataengineeringpodcast.com/materialize]([link] Support Data Engineering Podcast

Data Engineer 147

Data Engineer Data Engineering Engineering PostgreSQL 147

Hevo

JULY 18, 2024

The term “Data Lakehouse” is quite common nowadays. The new concept promises to address the failures of data warehouses and data lakes and help support the workloads of both business intelligence and machine learning. Data […]

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Data Engineering Podcast

DECEMBER 18, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

JANUARY 15, 2023

Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box.

Coding 130

Coding Building PostgreSQL Data Lake 130

Data Engineering Podcast

DECEMBER 29, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Management 130

Management Metadata Business Intelligence Data Lake 130

Data Engineering Podcast

JANUARY 8, 2023

Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Missing data? Missing data?

PostgreSQL 130

PostgreSQL Data Lake Data Warehouse BI 130

ProjectPro

JULY 22, 2016

For the same cost, organizations can now store 50 times as much data as in a Hadoop data lake than in a data warehouse. Data lake is gaining momentum across various organizations and everyone wants to know how to implement a data lake and why.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 40

Advancing Analytics: Data Engineering

JULY 2, 2019

It’s not always the most accurate indicator, but a quick glance at google trends sees Data Engineer rocketing in popularity, compared to more traditional functions such as BI and ETL Developer: google trends Now, that’s not saying that the other roles are going away, not by a long stretch. or visit www.advancinganalytics.co.uk

Business Intelligence 52

Business Intelligence Data Engineer Data Engineering Engineering 52

Striim

SEPTEMBER 11, 2024

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Marts There is a thin line between Data Warehouses and Data Marts.

Data Engineer 81

Data Engineer Data Engineering Engineering Data Lake 81

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 93

Data Lake Data Warehouse BI SQL 93

Data Engineering Podcast

NOVEMBER 27, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Data Process 100

Data Process Process Metadata Business Intelligence 100

RandomTrees

SEPTEMBER 27, 2024

A cloud-based software as a service (SaaS) called Microsoft Fabric combines several data and analytics technologies that businesses require. Data Factory, Data Activator, Power BI, Synapse Real-Time Analytics, Synapse Data Engineering, Synapse Data Science, and Synapse Data Warehouse are some of them.

Database-centric 52

Database-centric Pipeline-centric IT BI 52

Data Engineering Podcast

NOVEMBER 4, 2021

Summary The precursor to widespread adoption of cloud data warehouses was the creation of customer data platforms. Acting as a centralized repository of information about how your customers interact with your organization they drove a wave of analytics about how to improve products based on actual usage data.

Data Warehouse 100

Data Warehouse Business Intelligence ETL System Data Lake 100

Monte Carlo

JANUARY 16, 2024

At the center of it all is the data warehouse, the lynchpin of any modern data stack. In this blog post, we’ll look at six innovations that are shaping the future of the data warehousing, as well as challenges and considerations that organizations should keep in mind. Data lake and data warehouse convergence 2.

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

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.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Cloudera

JUNE 30, 2022

Over the past decade, Cloudera has enabled multi-function analytics on data lakes through the introduction of the Hive table format and Hive ACID. Companies, on the other hand, have continued to demand highly scalable and flexible analytic engines and services on the data lake, without vendor lock-in.

Data Lake 87

Data Lake Business Intelligence Metadata Data Warehouse 87

Data Engineering Podcast

DECEMBER 25, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value.

Building 100

Building Metadata Business Intelligence Data Lake 100

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 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

Data Engineering Podcast

SEPTEMBER 11, 2022

Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Can you describe what Schemata is and the story behind it?

Systems 100

Systems Metadata Building MongoDB 100

Data Engineering Podcast

DECEMBER 19, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Systems 100

Systems Building Metadata Data Warehouse 100

Data Engineering Podcast

MAY 8, 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.

Database 100

Database Data Lake BI Business Intelligence 100

Data Engineering Podcast

DECEMBER 11, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Warehouse 100

Data Warehouse Raw Data Data Lake BI 100

Data Engineering Podcast

JANUARY 1, 2022

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Warehouse 100

Data Warehouse SQL Hadoop Data Lake 100

Data Engineering Podcast

DECEMBER 4, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Warehouse 100

Data Warehouse Data Lake BI Data 100

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ).

Data Architecture 78

Data Architecture Architecture Data Lake NoSQL 78

Data Engineering Podcast

OCTOBER 28, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Pipeline 100

Data Pipeline SQL Data Warehouse Data Lake 100

Data Engineering Podcast

NOVEMBER 27, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! Hightouch is the easiest way to sync data into the platforms that your business teams rely on.

Data Warehouse 100

Data Warehouse Data Lake BI Business Intelligence 100

Data Engineering Podcast

NOVEMBER 25, 2018

Summary When your data lives in multiple locations, belonging to at least as many applications, it is exceedingly difficult to ask complex questions of it. The default way to manage this situation is by crafting pipelines that will extract the data from source systems and load it into a data lake or data warehouse.

Data Lake 100

Data Lake Data Warehouse Hadoop BI 100