Data Engineering Podcast

JANUARY 30, 2022

Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. The only thing worse than having bad data is not knowing that you have it.

Data Engineering 100

Data Engineering Data Engineer Engineering Data Pipeline 100

Knowledge Hut

MAY 23, 2024

Data science has become the fastest-growing field and a buzzword that every industry knows and leverages. This vast stream of interdisciplinary domains deals with data in different ways. It helps companies understand data and obtain meaningful insights from it. Each of these tools comes with a set of some of these usages.

Data Science 52

Data Science MongoDB Programming Language Unstructured Data 52

Knowledge Hut

MARCH 28, 2024

Data science has become one of the most trending fields today. Data engineering is one of them. According to AnalytixLabs , the data science market is expected to be worth USD 230.80 This demonstrates the increasing need for Microsoft Certified Data Engineers. Let’s get started.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Knowledge Hut

SEPTEMBER 25, 2023

This demonstrates how in-demand Microsoft Certified Data Engineers are becoming. They are moving their servers and on-premises data to Azure Cloud. What does all of this mean for Data Engineering professionals? Who is an Azure Data Engineer? Azure Data Engineers work with these and other solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

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

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

DareData

NOVEMBER 28, 2023

Nowadays, the next step for a Junior Data Scientist to get into real-life projects resides in understanding how to gather, manage and organize information on different high-performing machine learning models; deploy them into production; and monitor the performance. Want to get your Data Science teams to the next level?

Data Science 52

Data Science Medical Machine Learning Data 52

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

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. Apache Oozie — An open-source workflow scheduler system to manage Apache Hadoop jobs. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively.

Consulting 120

Consulting Machine Learning Data Science Data Pipeline 120

Databand.ai

AUGUST 30, 2023

Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Data Integration Data integration is the process of collecting, transforming, and consolidating data from various sources.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Databand.ai

AUGUST 30, 2023

It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time. This includes data ingestion, processing, storage, and analysis.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Monte Carlo

MARCH 24, 2021

As a new or aspiring data engineer, there are some essential technologies and frameworks you should know. How to build a data pipeline? How to clean, transform, and model your data? How to prevent broken data workflows before you get that frantic call from your CEO about her missing data?

Data Engineering 40

Data Engineering Data Engineer Engineering Software Engineer 40

Monte Carlo

JULY 27, 2023

A financial dashboard may need to be accurate within a penny while a machine learning model may only need directionally accurate (but constantly refreshed) data. The data quality engineer will then need to design or optimize data architectures and pipelines to ensure these requirements are met (mentioned in 70% of job postings).

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Data use component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

PostgreSQL 52

PostgreSQL Metadata Python MySQL 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

DataKitchen

JANUARY 3, 2022

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98