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Choosing A Graph Data Model to Best Serve Your Use Case

Ontotext

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships. This makes LPGs inflexible.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

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Automating Data Pipelines in CDP with CDE Managed Airflow Service

Cloudera

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Typically users need to ingest data, transform it into optimal format with quality checks, and optimize querying of the data by visual analytics tool.

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Data Prep for AI: Get Your Oracle House in Order

Jet Global

Merging these datasets requires not just combining the data, but also complex transformations to ensure corresponding fields like customer and product information align seamlessly. Additionally, Oracle Cloud ERP’s AI models might have specific data format requirements.

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The Journey to DataOps Success: Key Takeaways from Transformation Trailblazers

DataKitchen

Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Similarly, Workiva was driven to DataOps due to an increased need for analytics agility to meet a range of organizational needs, such as real-time dashboard updates or ML model training and monitoring.

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AzureML and CRISP-DM – a Framework to help the Business Intelligence professional move to AI

Jen Stirrup

They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way. Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

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What is a Data Pipeline?

Jet Global

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.