Striim

JULY 10, 2024

UPS Capital integrated Striim’s real-time data streaming with Google BigQuery’s analytics to enhance delivery security through immediate data ingestion and real-time risk assessments. That’s where Striim came into play.

Management 52

Management Transportation Machine Learning High Quality Data 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. 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 69

Raw Data Data Business Intelligence Data Engineering 69

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 52

Data Pipeline Machine Learning High Quality Data Data Ingestion 52

Data Engineering Weekly

NOVEMBER 13, 2022

It moved from the speculation to the data engineers understanding the benefit of it and asking when we can get the implementation soon. I met many data leaders about Data Contracts, my project Schemata, and how the extended version we are building can help them create high-quality data.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

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? Empty queries could create data integrity issues.

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

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 40

Data Ingestion Data Government Datasets 40

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Data observability in the data lakehouse Data lakehouse architecture is a prominent innovation in data management that continues to evolve.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. Data observability in the data lakehouse Data lakehouse architecture is a prominent innovation in data management that continues to evolve.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Data Science Blog: Data Engineering

AUGUST 22, 2024

Its platform is designed to handle the complexities of modern data environments, offering features that align with the latest trends in data preparation. In conclusion, as generative AI continues to influence industries, the need for high-quality data is important.

Data Preparation 52

Data Preparation Transportation High Quality Data Data Science 52

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.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Knowledge Hut

APRIL 23, 2024

Evaluate Data Velocity: Determine the speed at which data is generated, collected, and processed in your context. Consider technologies and methodologies that enable rapid data ingestion, streaming analytics, and timely decision-making. Ensure Data Veracity: Prioritize data quality and reliability.

Big Data 52

Big Data Media Datasets Unstructured Data 52

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.

Kafka 52

Kafka Algorithm Data Pipeline Data 52

Monte Carlo

MAY 25, 2023

Data orchestration helps companies comply with various international privacy laws and regulations, many of which require companies to demonstrate the source and rationale for their data collection. As data volume grows, scheduling becomes critical to successfully managing your data ingestion and transformation jobs.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Edureka

FEBRUARY 7, 2023

To prepare for the exam, you should have hands-on experience using Azure data services to design and build data engineering solutions. It covers topics such as data ingestion, data transformation, and data delivery, as well as data storage, data processing, and data security.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

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 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

AUGUST 8, 2023

From this perspective, the data validation process looks a lot like any other DataOps process. If you choose the wrong approach, no number of data validation tests will save you from the perception of poor data quality. Writing tests that pass isn’t a useful process and of itself, it’s a means to an end.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

DataKitchen

NOVEMBER 27, 2024

Organizations can take targeted action by identifying which suppliers, systems, or processes are contributing to poor-quality data, whether that means renegotiating contracts with external vendors, improving internal workflows, or implementing better validation processes at the point of ingestion.

Banking 86

Banking Data Datasets Retail 86