AltexSoft

SEPTEMBER 19, 2023

By harnessing internal data sources, travel businesses can develop personalized services that cater to their customers’ ever-evolving needs, as well as optimize their operations, enhance marketing strategies, and improve resource allocation, ultimately driving growth and profitability. geolocation, profiles, and feedback.

Hospitality 52

Hospitality Machine Learning Data Governance BI 52

AltexSoft

MARCH 14, 2023

It must collect, analyze, and leverage large amounts of customer data from various sources, including booking history from a CRM system, search queries tracked with Google Analytics, and social media interactions. This means that companies don’t necessarily need a large data engineering team. Data democratization.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Data Engineering Podcast

NOVEMBER 9, 2020

He also shares the internal architecture, how he approached the design to make it accessible and easy to use, and how it autodiscovers the schemas and metadata for your source systems. If you hand a book to a new data engineer, what wisdom would you add to it?

Building 100

Building PostgreSQL BI Metadata 100

dbt Developer Hub

JULY 13, 2022

Because of this, the ability to access a full picture of historical outputs isn't intrinsic to dbt. Using dbt and her favorite BI tool, Joanne has created an income report to track monthly income for each product category. If you’ve encountered this problem before, you know it’s a tricky one. Let’s imagine a specific scenario.

Electronics 59

Electronics BI Raw Data Manufacturing 59

Data Engineering Podcast

SEPTEMBER 28, 2020

To make the benefits of the Kafka ecosystem more accessible and reduce the operational burden, Alexander Gallego and his team at Vectorized created the Red Panda engine. If you hand a book to a new data engineer, what wisdom would you add to it? If you hand a book to a new data engineer, what wisdom would you add to it?

Kafka 100

Kafka BI Big Data Data Engineering 100

AltexSoft

JUNE 26, 2023

Commonly, the entire flow is fully automated and consists of three main steps — data extraction, transformation, and loading ( ETL or ELT , for short, depending on the order of the operations.) Dive deeper into the subject by reading our article Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Data Engineering Podcast

NOVEMBER 30, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Data Integration 100

Data Integration Coding BI Kafka 100

Data Engineering Podcast

NOVEMBER 23, 2020

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it?

Hadoop 100

Hadoop Data Pipeline BI Data 100

Monte Carlo

JANUARY 23, 2024

Data engineering was a thankless but increasingly important job, with data engineering teams straddling between building infrastructure, running jobs, and fielding ad-hoc requests from the analytics and BI teams. As a result, being a data engineer was both a blessing and a curse. What do you see in yours? We’re all ears.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

APRIL 20, 2023

Overwhelmed data engineers need to have the proper context of the blast radius to understand which incidents need to be addressed right away, and which incidents are a secondary priority. This is one of the most frequent data lineage use cases leveraged by Vox.

Data Warehouse 52

Data Warehouse BI Data Government 52

Monte Carlo

JANUARY 23, 2023

They allow organizations to ingest and manage large volumes of data in an aggregated storage solution when they might not be entirely sure how they’ll use that data in the future, whether for business intelligence or data products. If a data lake is one approach to data architecture, a data mesh is its philosophical opposite.

Data Lake 52

Data Lake Unstructured Data Business Intelligence Architecture 52

dbt Developer Hub

JULY 2, 2023

Data Vault 2.0 is a data modeling technique designed to help scale large data warehousing projects. It is a rigid, prescriptive system detailed vigorously in a book that has become the bible for this technique. So why Data Vault?

Cloud 52

Cloud Data Warehouse Data BI 52

Monte Carlo

APRIL 1, 2021

Over the past few decades, the world of data analytics has undergone transformation from a siloed entity to a cross-functional powerhouse. Now, in 2021, in this decade of data, the time is ripe for yet another sea-change, this time in the form of data democratization and accessibility. What was your favorite book in 2020?

Building 52

Building BI Business Intelligence Data 52

Monte Carlo

APRIL 29, 2021

With this partnership, Snowflake customers can now: Achieve end-to-end Data Observability without writing any code. Get end-to-end data observability for your Snowflake data pipelines with a 20-minute, no-code implementation process. Know when data breaks, as soon as it happens.

Insurance 40

Insurance Business Intelligence Healthcare Data Warehouse 40

Knowledge Hut

SEPTEMBER 6, 2023

Over the past decade, the IT world transformed with a data revolution. Back when I studied Computer Science in the early 2000s, databases like MS Access and Oracle ruled. The rise of big data and NoSQL changed the game. Systems evolved from simple to complex, and we had to split how we find data from where we store it.

Database 52

Database Certification MongoDB MySQL 52

Monte Carlo

MAY 17, 2023

Now, data engineers barely have to investigate the issue because the root cause is right in front of you,” said Kineret Kimhi, BI and Data Engineering Manager, BlaBlaCar. Choozle also saw an 80% reduction in overall data downtime as a result of data observability. “We It was very time-consuming.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Now, data engineers barely have to investigate the issue because the root cause is right in front of you,” said Kineret Kimhi, BI and Data Engineering Manager, BlaBlaCar. Choozle also saw an 80% reduction in overall data downtime as a result of data observability. “We It was very time-consuming.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

Monte Carlo

APRIL 7, 2021

As the PM for Protocols , our data governance tool, I spent a lot of time thinking about and building dashboards to evaluate the quality of data our customers were sending to Segment. Your data lineage should include data products like BI reports, ML models or reverse ETL sinks to be useful.

Data Engineering 40

Data Engineering Data Engineer Engineering Datasets 40

Databand.ai

DECEMBER 20, 2022

Ken spreads this knowledge on his YouTube channel and on his Medium blog , where his content provides insight into the data science community, career advice, and sports related analysis. On LinkedIn, he posts regularly about data analytics and data science.

Data Analytics 52

Data Analytics Google Cloud Data Science Data Mining 52

ProjectPro

AUGUST 24, 2021

Data Factory to orchestrate regular runs of the Azure Machine Learning model. Power BI dashboard to display and drill down the predictions. 4) Event Data Analysis NYC Open Data is free public data published by New York City agencies and partners. Machine Learning web service to host forecasting code.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52