Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Data Governance 59

Data Governance ML ML Cloud Data 59

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. Integrated data catalog for metadata support As you build out your IT ecosystem, it is important to leverage tools that have the capabilities to support forward-looking use cases. A notable capability that achieves this is the data catalog.

Data Governance 64

Data Governance ETL Data Pipeline Azure 64

Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Precisely

MARCH 14, 2023

First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. Instead of moving customer data to the processing engine, we move the processing engine to the data. So how did providers respond?

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. Integrated data catalog for metadata support As you build out your IT ecosystem, it is important to leverage tools that have the capabilities to support forward-looking use cases. A notable capability that achieves this is the data catalog.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

phData

AUGUST 4, 2023

The external stage area includes Microsoft Azure Blob storage, Amazon AWS S3, and Google Cloud Storage. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions. The data can then be processed using Snowflake’s SQL capabilities.

Database 52

Database Azure SQL AWS 52

The MLOps Blog

MARCH 20, 2023

However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams. Data aggregation: Data sources could increase as more data points are required to train ML models. Existing data pipelines will have to be modified to accommodate new data sources.

ML 59

ML ML Machine Learning Machine Learning 59

Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Data Security and Governance Maintaining data security is crucial for any company.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JULY 18, 2023

Better Transparency: There’s more clarity about where data is coming from, where it’s going, why it’s being transformed, and how it’s being used. Improved Data Governance: This level of transparency can also enhance data governance and control mechanisms in the new data system.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

phData

AUGUST 17, 2023

Data pipeline orchestration tools are designed to automate and manage the execution of data pipelines. These tools help streamline and schedule data movement and processing tasks, ensuring efficient and reliable data flow. What are Orchestration Tools?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52