Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 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

Databand.ai

AUGUST 30, 2023

DataOps also encourages a culture of continuous improvement and innovation, as teams work together to identify and address bottlenecks and inefficiencies in their data pipelines and processes. This can be achieved through the use of automated data ingestion, transformation, and analysis tools.

Data Governance 52

Data Governance Data Pipeline Government Business Analyst 52

Towards Data Science

JULY 8, 2024

Leveraging TensorFlow Transform for scaling data pipelines for production environments Photo by Suzanne D. Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. ML Pipeline operations begins with data ingestion and validation, followed by transformation.

Data Preparation 70

Data Preparation Datasets Metadata Data Ingestion 70

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

RandomTrees

SEPTEMBER 6, 2020

The data ingestion cycle usually comes with a few challenges like high data ingestion cost, longer wait time before analytics is performed, varying standard for data ingestion, quality assurance and business analysis of data not being sustained, impact of change bearing heavy cost and slow execution.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Data Engineering Weekly

OCTOBER 30, 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 Data Ingestion 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. The current landscape of Data Observability Tools shows a marked focus on “Data in Place,” leaving a significant gap in the “Data in Use.”

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Monte Carlo

OCTOBER 5, 2023

Data freshness (aka data timeliness) means your data should be up-to-date and relevant to the timeframe of analysis. Data validity means your data conforms to the required format, type, or range of values. Example: Email addresses in the customer database should match a valid format (e.g.,

Government 52

Government Data Governance Data Cloud Storage 52

phData: Data Engineering

JANUARY 3, 2022

How do I know where this data came from or how it’s being used? How do I maintain all my data pipelines? How do I recreate the environment and data sets from scratch? How do I build confidence and trust in the data products I create? How do I ensure customers aren’t impacted by changes or new functionality?

IT 52

IT AWS Software Engineer Software Engineering 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Enriching data entails connecting it to other related data to produce deeper insights.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Striim

AUGUST 28, 2024

The notion that real-time data is only useful in specific cases is outdated as countless industries increasingly leverage real-time capabilities to stay competitive and responsive. Misconception: Complexity and Cost Objection: Implementing real-time data systems is complex and costly.

Healthcare 96

Healthcare Retail Algorithm Finance 96

Striim

JUNE 17, 2024

The notion that real-time data is only useful in specific cases is outdated as countless industries increasingly leverage real-time capabilities to stay competitive and responsive.” ” Complexity and Cost Objection: Implementing real-time data systems is complex and costly.

Algorithm 52

Algorithm Retail Data Ingestion Healthcare 52