Monte Carlo

FEBRUARY 20, 2024

Consideration What to Look For Integration Capabilities Support for a diverse array of data sources and destinations, ensuring compatibility with your data ecosystem. Batch vs. Streaming Assess if your data processing leans towards real-time analytics or if batch processing suffices for your use case.

Data Ingestion 52

Data Ingestion Google Cloud Kafka AWS 52

DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Knowledge Hut

APRIL 23, 2024

Gathering data at high velocities necessitates capturing and ingesting data streams as they occur, ensuring timely acquisition and availability for analysis. Utilizing is related to the data processing and analyzing speed for gleaning useful insights. Consider both structured and unstructured data.

Big Data 52

Big Data Media Datasets Unstructured Data 52

Knowledge Hut

MARCH 5, 2024

A Data Engineer's core responsibility is to process large amounts of data and optimize its storage. Data engineers also analyze the kind of data that should be retrieved and stored. Data engineers in the technology industry focus on data streaming and data processing pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Education 52

Workfall

JULY 4, 2023

With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Knowledge Hut

JUNE 28, 2023

Reporting, querying, and analyzing structured data to generate actionable insights. Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data. Structured data from databases, data warehouses, and operational systems.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Machine Learning can then use this data to train, test, and deploy machine learning models. It does the job.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Databand.ai

JULY 5, 2023

Metric collection and visualization: Gathering metrics from various sources, such as logs, APIs, and databases, is crucial for effectively monitoring system performance. Notable Data Observability Tools Here are a few notable data observability tools.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

DataKitchen

OCTOBER 2, 2023

This comprehensive article delves into the complexities encountered by various types of data teams—Data Ingestion Teams, End-to-End Data Product Teams, and Enterprise Data Enablement Teams—to name a few. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Databand.ai

JULY 10, 2023

End-to-end visibility: These platforms provide end-to-end visibility into systems, allowing for the monitoring of every aspect from front-end user interactions to back-end processing and database transactions. Together, they offer a powerful solution for collecting, processing, analyzing, and visualizing data from various sources.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Aggregated Data 52

ProjectPro

FEBRUARY 21, 2023

By mastering the fundamentals, you can advance professionally and pursue other technical opportunities with Azure, such as Associate Azure Data Engineer , Associate Azure Database Administrator, etc. A score of 700 is required to pass the Azure Data Fundamentals exam, graded on a scale of 1 to 1000.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

AUGUST 22, 2022

What is data fabric? A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., Source: Towards Data Science.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Ascend.io

JUNE 8, 2023

Data catalogs are an efficient way to vet datasets with data stewards, and make them publicly available so stakeholders can discover and self-serve data they need and review attributes and lineage to confirm their choices. Good data stewardship and healthy data catalogs are worthwhile investments.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Databand.ai

JANUARY 24, 2023

As a result, DataOps observability is an essential part of maintaining high quality data and ensuring that data practices are as efficient and effective as possible. In doing so, it allows teams to identify problems faster and, therefore, deliver solutions faster. Who’s Involved in a DataOps Team?

Retail 52

Retail Banking Government Manufacturing 52

U-Next

SEPTEMBER 29, 2022

A structured data record consists of a very fixed field of data. Relational databases, spreadsheets, and other documents can contain this type of data. There is also the possibility of semi-structured data being a cross between these two types of data. Step 2: Build data literacy and confidence .

Management 52

Management Unstructured Data Data Government 52