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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

It also supports a wide range of data warehouses, analytical databases, data lakes, frontends, and pipelines/ETL. This includes the creation of SQL Code, DACPAC files, SSIS packages, Data Factory ARM templates, and XMLA files. Pipelines/ETL : It supports SQL Server Integration Packages (SSIS), Azure Data Factory 2.0

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference. Previously, data scientists often found themselves juggling multiple tools to support SQL in their workflow, which hindered productivity.

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How to choose a graph database: we compare 6 favorites

Cambridge Intelligence

That’s why our data visualization SDKs are database agnostic: so you’re free to choose the right stack for your application. There have been a lot of new entrants and innovations in the graph database category, with some vendors slowly dipping below the radar, or always staying on the periphery. can handle many graph-type problems.

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The ultimate guide to Hyper-V backups for VMware administrators

Data Science Dojo

Windows Failover Clustering is applied in a number of different use cases, including file servers and SQL clusters, as well as Hyper-V. vmsd – This database file contains all the pertinent snapshot information. .vmsn It replaces the XML file found in 2012 R2 and earlier. AVHDX – This is the differencing disk that is created.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

It was built using a combination of in-house and external cloud services on Microsoft Azure for large language models (LLMs), Pinecone for vectorized databases, and Amazon Elastic Compute Cloud (Amazon EC2) for embeddings. Opportunities for innovation CreditAI by Octus version 1.x x uses Retrieval Augmented Generation (RAG).

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

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How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)

IBM Journey to AI blog

Netezza Performance Server (NPS) has recently added the ability to access Parquet files by defining a Parquet file as an external table in the database. All SQL and Python code is executed against the NPS database using Jupyter notebooks, which capture query output and graphing of results during the analysis phase of the demonstration.