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What’s New with Azure Databricks: Unified Governance, Open Formats, and AI-Native Workloads

databricks

Cross-cloud data governance with Unity Catalog supports accessing S3 data from Azure Databricks. This enables organizations to enforce consistent security, auditing, and data lineage across cloud boundaries. Lakebridge accelerates the migration of legacy data warehouse workloads to Azure Databricks SQL.

Azure 238
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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form. Deployment and Monitoring Once a model is built, it is moved to production.

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How to Split Text For Vector Embeddings in Snowflake

phData

“ Vector Databases are completely different from your cloud data warehouse.” – You might have heard that statement if you are involved in creating vector embeddings for your RAG-based Gen AI applications. This process is repeated until the entire text is divided into coherent segments. Return the chunks as an ARRAY.

Python 52
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Top 10 Python Scripts for use in Matillion for Snowflake

phData

One such option is the availability of Python Components in Matillion ETL, which allows us to run Python code inside the Matillion instance. In this blog, we will describe 10 such Python Scripts that can provide a blueprint for using the Python component efficiently in Matillion ETL for Snowflake AI Data Cloud.

Python 52
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Optimizing Matillion Workflows: A Guide to Visual Design and Best Practices

phData

A Matillion pipeline is a collection of jobs that extract, load, and transform (ETL/ELT) data from various sources into a target system, such as a cloud data warehouse like Snowflake. Intuitive Workflow Design Workflows should be easy to follow and visually organized, much like clean, well-structured SQL or Python code.

AI 52
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Why using Infrastructure as Code for developing Cloud-based Data Warehouse Systems?

Data Science Blog

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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Cloud Data Science 11

Data Science 101

Even with the coronavirus causing mass closures, there are still some big announcements in the cloud data science world. Google introduces Cloud AI Platform Pipelines Google Cloud now provides a way to deploy repeatable machine learning pipelines. Azure Functions now support Python 3.8 So, here is the news.