Data Engineering Podcast

FEBRUARY 24, 2020

Are you spending too much time maintaining your data pipeline? Snowplow empowers your business with a real-time event data pipeline running in your own cloud account without the hassle of maintenance. What are some of the ways that compliance or data quality issues can arise from these projects?

IT 100

IT Data Lake Data Pipeline Media 100

Monte Carlo

JULY 11, 2023

In other words, is it likely your data is accurate based on your expectations? Data collection methods: Understand the methodology used to collect the data. Look for potential biases, flaws, or limitations in the data collection process. is the gas station actually where the map says it is?).

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Monte Carlo

OCTOBER 5, 2023

This collaborative approach helps teams better understand the full scope of data quality, balancing technical intricacies with its broader strategic relevance. Data quality standards for governance Data governance is the process of maintaining the availability, usability, privacy, and security of data.

Government 52

Government Data Governance Data Datasets 52

DareData

JULY 5, 2023

Users: Who are users that will interact with your data and what's their technical proficiency? Data Sources: How different are your data sources? Latency: What is the minimum expected latency between data collection and analytics? And what is their format?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

DataKitchen

JANUARY 23, 2023

Data lineage can also be used for compliance, auditing, and data governance purposes. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. What is missing in data lineage?

Data Governance 52

Data Governance Government Data Pipeline Data 52

Databand.ai

JULY 3, 2023

Invalid data can lead to a variety of issues, such as application errors, incorrect data analysis results, and overall poor data quality. The key issues and challenges associated with data reliability include: Data collection : The way data is collected can greatly affect its reliability.

Data Validation 52

Data Validation Data Collection Data Machine Learning 52

phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Monte Carlo

MARCH 28, 2023

How do we instill the sense of quality and timeliness in the data?” How does stale data happen and what are the consequences? Stale data can be the result of various factors, such as outdated data collection processes, bad SQL queries, and broken data pipelines.

IT 52

IT Machine Learning Data Business Intelligence 52

Monte Carlo

MARCH 22, 2023

This data quality framework has five data quality dimensions – assurances of integrity, methodological soundness, accuracy and reliability, serviceability, and accessibility. Weaknesses: Primarily tailored for statistical data, making it less suitable for other types of data. Lacks a data governance focus.

Consulting 52

Consulting Data Governance Data Government 52

Monte Carlo

MAY 18, 2023

Whether you’re a data scientist, data engineer, or business analyst, keeping track of your data’s origin, transformation, and movement is crucial for maintaining transparency, enforcing data governance, and ensuring data quality. Why do you need data lineage?

Metadata 64

Metadata Data Lake Government Data Governance 64

Snowflake

OCTOBER 25, 2023

While behavioral data is important, it’s rarely the only type of data needed to properly train an AI model for marketing purposes. If your behavioral data is siloed, organizations may be forced to build data pipelines to support AI model training on a comprehensive corpus of required data.

Government 88

Government Accessible Accessibility Technology 88

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? This privacy law must be kept in mind when building data architecture.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Data Engineering Weekly

NOVEMBER 13, 2022

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. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Knowledge Hut

JULY 3, 2023

Real-time Data ingestion performs the utilization of data from various origins, does the data cleaning, validation, and preprocessing operations and at the end store it in the required format, either structured or unstructured. As real-time insights gain popularity, real-time data ingestion remains vital for companies worldwide.

Data Ingestion 52

Data Ingestion Pipeline-centric Google Cloud Media 52

Snowflake

APRIL 20, 2023

The latest data is regularly pulled from ServiceNow, and you control how frequently it is refreshed. Data Pipelines Snowpipe Streaming – public preview While data generated in real time is valuable, it is more valuable when paired with historical data that helps provide context.

Medical 57

Medical Retail Python Pharmaceutical 57

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Pipeline Data Warehouse AWS 52

Monte Carlo

JULY 30, 2020

Unlike traditional monolithic data infrastructures that handle the consumption, storage, transformation, and output of data in one central data lake, a data mesh supports distributed, domain-specific data consumers and views “data-as-a-product,” with each domain handling their own data pipelines.

IT 45

IT Data Lake Business Intelligence Data Architecture 45

Databand.ai

AUGUST 30, 2023

In a world where organizations rely heavily on data observability for informed decision-making, effective data testing methods are crucial to ensure high-quality standards across all stages of the data lifecycle—from data collection and storage to processing and analysis.

Data Validation 52

Data Validation Data Integration Data Database 52

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. How to get started with data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

Knowledge Hut

JANUARY 30, 2024

Audio Transcription: Audio data can be transcribed into text using speech recognition technology, enabling the extraction of spoken content for analysis, such as customer service call logs or voice recordings. Overcoming these challenges requires a combination of technical expertise, robust processes, and a commitment to data governance.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Also, explore other alternatives like Apache Hadoop and Spark RDD.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52