Data Ingestion, Data Pipeline and High Quality Data

Data Ingestion

Data Pipeline

High Quality Data

8 Data Ingestion Tools (Quick Reference Guide)

Monte Carlo

FEBRUARY 20, 2024

At the heart of every data-driven decision is a deceptively simple question: How do you get the right data to the right place at the right time? The growing field of data ingestion tools offers a range of answers, each with implications to ponder. Fivetran Image courtesy of Fivetran.

Data Ingestion

Data Ingestion Google Cloud Kafka AWS

Build vs Buy Data Pipeline Guide

Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform.

Data Pipeline

Data Pipeline Building Data Ingestion BI

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

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Creating Value With a Data-Centric Culture: Essential Capabilities to Treat Data as a Product

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

Pipeline-centric Database-centric Data Ingestion Data Pipeline

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

MORE WEBINARS

DataOps vs. MLOps: Similarities, Differences, and How to Choose

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. Better data observability equals better data quality.

Data Pipeline

Data Pipeline Machine Learning High Quality Data Data Ingestion

AI Implementation: The Roadmap to Leveraging AI in Your Organization

Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline

Data Pipeline Government Data Governance Raw Data

Data Integrity vs. Data Validity: Key Differences with a Zoo Analogy

Monte Carlo

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? What Is Data Validity?

Data Validation

Data Validation Data Integration Data Cleanse Data Pipeline

Data Teams and Their Types of Data Journeys

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion

Data Ingestion Data Government Datasets

What is Data Orchestration?

Monte Carlo

MAY 25, 2023

Picture this: your data is scattered. Data pipelines originate in multiple places and terminate in various silos across your organization. Your data is inconsistent, ungoverned, inaccessible, and difficult to use. Some of the value companies can generate from data orchestration tools include: Faster time-to-insights.

Data Pipeline

Data Pipeline Data Workflow Data Data Governance

How to become Azure Data Engineer I Edureka

Edureka

FEBRUARY 7, 2023

Azure Data Engineers use a variety of Azure data services, such as Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics, and Azure Databricks, to design and implement data solutions that meet the needs of their organization. Gain hands-on experience using Azure data services.

Data Engineering

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How Tenable Built a Reliable Data Platform at Terabyte Scale with Monte Carlo

Monte Carlo

JUNE 12, 2023

Like many companies, Tenable’s data team structure has shifted over time, but they currently have a centralized data platform team that includes data engineering, data science, and a data ingestion team. The team built out manual tests to address data quality issues as they came up.

Kafka

Kafka Algorithm Data Pipeline High Quality Data

Data Engineering Weekly #107

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

Data Engineering Data Engineer Engineering Kafka

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

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. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. What is Data in Use?

Raw Data

Raw Data Data Business Intelligence High Quality Data

Data Validation Testing: Techniques, Examples, & Tools

Monte Carlo

AUGUST 8, 2023

By applying rules and checks, data validation testing verifies the data meets predefined standards and business requirements to help prevent data quality issues and data downtime. From this perspective, the data validation process looks a lot like any other DataOps process.

Data Validation

Data Validation Data Pipeline SQL Data

Forge Your Career Path with Best Data Engineering Certifications

ProjectPro

FEBRUARY 21, 2023

Google Cloud Certified Professional Data Engineer Certifications An individual is fit for taking the GCP Data Engineering certification exam if he/she- Has more than three years of prior data engineering experience, including at least one year of solution design and management using Google Cloud.

Certification

Certification Data Engineering Data Engineer Engineering

Data Engineering Digest

8 Data Ingestion Tools (Quick Reference Guide)

Build vs Buy Data Pipeline Guide

Webinars

Trending Sources

Creating Value With a Data-Centric Culture: Essential Capabilities to Treat Data as a Product

Webinars

DataOps vs. MLOps: Similarities, Differences, and How to Choose

AI Implementation: The Roadmap to Leveraging AI in Your Organization

Data Integrity vs. Data Validity: Key Differences with a Zoo Analogy

Data Teams and Their Types of Data Journeys

What is Data Orchestration?

How to become Azure Data Engineer I Edureka

How Tenable Built a Reliable Data Platform at Terabyte Scale with Monte Carlo

Data Engineering Weekly #107

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

Data Validation Testing: Techniques, Examples, & Tools

Forge Your Career Path with Best Data Engineering Certifications

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