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Cloudblog
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Announcing the general availability of Spanner Graph
- Building truly intelligent applications requires more than just raw data. Graph analysis helps reveal connections and when combined with other techniques enables AI-enabled application experiences. Spanner Graph reimagines graph data management with a unified database that integrates graph, relational, search, and gen AI capabilities with virtually unlimited scalability. With Spanner Graph, you gain access to an intuitive ISO Standard Graph Query Language (GQL) interface that simplifies pattern matching and relationship traversal. You also benefit from full interoperability between GQL and SQL. Integration with Vertex AI gives you access to powerful AI models directly within Spanner Graph.
- Spanner Graph is now generally available and provides an exciting range of capabilities and integrations. The new open-source Spanner Graph Notebook tool provides an efficient way to query Spanner Graph visually. Spanner Graph's integration with LangChain allows for quick prototyping of GraphRAG applications. Spanner Studio Explorer panel now displays a list of defined graphs. Graph visualization partner integrations include GraphXR, Kineviz, Graphistry's GPU-accelerated visual graph intelligence platform, and G.V().
- Spanner Graph now supports the path data type and functions, allowing you to retrieve and analyze the specific sequence of nodes and relationships that connect two nodes in your graph. Spanner Graph customers can use GraphXR, Kineviz's flagship product to make sense of complex, connected data. Other integrations include Graphistry's GPU-accelerated visual graph intelligence platform and G.V().
- Equifax uses Spanner Graph to manage commercial credit data for more than 30 million US businesses. Transmit Security integrates SQL for transactional data management with advanced graph data analysis to manage and analyze evaluated fraud data. U-NEXT leverages Spanner Graph for its flexible data modeling and high-performance querying.
- With Spanner Graph, users gain access to some of the world's most powerful and flexible graph data management tools. You can learn more about Spanner Graph's benefits and use cases and use their quick setup guide to get started. Try out some of their sample applications related to financial investing, fraud detection, and customer 360 and product recommendations.
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Siliconangle
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Oracle and Google expand service availability and add cross-region replication for disaster recovery
- Oracle Corp. and Google LLC’s cloud unit today announced an expanded partnership with broader regional coverage, additional services aimed at disaster recovery and a low-cost entry offering for customers that want to adopt Oracle’s Exadata high-performance database platform.
- Oracle Database@Google Cloud, which is a version of Oracle Cloud Infrastructure that runs natively on Google Cloud, will expand to eight new regions and data center capacity will also be doubled in some regions.
- Benefits of running Oracle software on the Google Cloud Platform include simplification of workload deployment and easy purchasing and contracting via Google Cloud Marketplace using existing Google Cloud commitments with unified support.
- New cross-region disaster recovery support allows customers to set up a replicated standby database in a separate Google Cloud region, while Oracle autonomous database manages itself and also cross-region recovery.
- The new single-node virtual machine cluster offering for Oracle Exadata Database Service on Dedicated Infrastructure provides a cheaper version of Exadata to customers with lower processing demands.
- Oracle Database@Google Cloud customers can purchase Oracle Database services using their existing Google Cloud commitments and leverage existing Oracle license benefits.
- The expansion also gives Oracle access to businesses in some regions where regulations or practical concerns require services to be delivered locally.
- Oracle Autonomous Database Serverless edition has been enhanced with cross-region disaster recovery and database replication and can be used while accessing Google services such as Vertex AI and BigQuery.
- Exadata for less - the move continues Oracle’s efforts to lower entry barriers to customers that want to adopt Exadata in the cloud.
- Oracle Cloud Infrastructure announced yesterday that it is expanding its reach to help global customers unlock new cloud capabilities.
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Hackernoon
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Here's How to Keep Tempdb From Becoming Your Worst Nightmare
- The unsung hero of many operations in SQL Server is the tempdb database.
- This guide helps diagnose and resolve tempdb issues before they lead to outages.
- The first query can help identify what is currently consuming space in tempdb.
- The second query reveals the hourly growth trend to catch runaway queries or misconfigured auto-grow settings.
- The third query helps identify potential top offenders by user or session and who is consuming tempdb.
- The fourth query helps identify orphaned or oversized temp tables in tempdb.
- The fifth query helps monitor space used by version store for snapshot isolation.
- The article provides proactive tuning recommendations such as pre-sizing tempdb, multiple data files, avoiding SELECT INTO and enabling query store.
- By systematically analyzing tempdb usage, one can address space issues, optimize query performance and ensure smooth SQL Server operations.
- It is recommended to regularly monitor key DMVs and automate alerts for tempdb growth to prevent problems.
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Dev
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What is the difference between "INNER JOIN" and "OUTER JOIN"?
- An inner join of A and B gives the result of A intersect B.
- An outer join of A and B gives the results of A union B.
- An inner join gives the intersection of the two tables, i.e. the common rows.
- An outer join gives all rows from one table plus the common rows from the other table.
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VoIP
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Oracle and Starlink Partnership Boosts Enterprise Communications with Satellites
- Oracle has partnered with Starlink to enhance its Enterprise Communications Platform (ECP) by integrating satellite connectivity.
- The collaboration aims to enable seamless connections to digital cloud applications, handling real-time video, audio, and IoT data.
- The ECP provides infrastructure for various industries, offering real-time data flow, secure video/audio streaming, and remote operation.
- The integration of Starlink improves Oracle's service by providing secure and faster broadband globally, revolutionizing business operations.
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Digitaltrends
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Stargate Project: everything you need to know about OpenAI’s $500 billion gamble
- Stargate Project is a new company led by OpenAI that aims to invest $500 billion over the next four years to build AI infrastructure in the US.
- The company will build data centers and power production plants across the US in order to secure American leadership in AI and create hundreds of thousands of American jobs.
- It is financially backed by SoftBank, OpenAI, Oracle and UAE-based firm MGX. Arm, Microsoft, Nvidia and OpenAI will serve as technology partners to the project.
- Stargate Project already broke ground on its first data center in Abilene, Texas, which is expected to cost $1.1 billion and to be completed by the end of 2025.
- Each data center should employ just under 60 people, but the company estimates it will create 100,000 jobs in total over the next four years.
- The project has been controversial, receiving criticism from Tesla CEO Elon Musk, who called it 'a sham', and former OpenAI CEO Sam Altman.
- The American AI industry is currently facing a crisis after the release of DeepSeek's AI models that offer performance similar to OpenAI's models, but at a fraction of the cost.
- If DeepSeek's models prove legitimate, Stargate might have little reason to continue investing in infrastructure.
- Amidst controversies, OpenAI continues to release AI models, and has now opened up developer access to full o1 reasoning model.
- OpenAI's next-generation o3 reasoning model has also been teased by the company.
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Amazon
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How Aqua Security exports query data from Amazon Aurora to deliver value to their customers at scale
- Aqua Security faced the challenge of efficiently exporting and analyzing large volumes of data to meet business requirements, including exporting and querying data at scale to share with customers for continuous monitoring and security analysis. Aqua needed a robust and scalable solution to export and share large volumes of security query data efficiently with their customers.
- To achieve an efficient and scalable data export, Aqua implemented parallelization techniques using the Step Functions map state and the aws_s3.query_export_to_s3 function.
- Aqua used Step Functions to orchestrate a serverless solution for exporting security query data from Aurora to Amazon S3, providing built-in error handling, automatic retries, fault tolerance, and detailed execution logs for efficient troubleshooting.
- The pipeline consists of the scheduler AWS Lambda function, the query exporter Lambda function that gets the required data and format it from the Aurora database using the query_export_to_s3 function, the poller Lambda function to retrieve the job's status, and the transferer Lambda function to transfer the output data from S3 to the customer’s predefined sink.
- The aws_s3.query_export_to_s3 function enabled Aqua to export the query output data split into files of 6 GB each, directly to an S3 bucket that was highly scalable and cost-effective data storage service. This function lacks the ability to track the status of the query export job.
- By integrating Step Functions, Lambda, Aurora, and the aws_s3.query_export_to_s3 function, Aqua achieved a highly scalable, automated, and cost-effective solution for exporting and storing security query data at scale.
- Aqua implemented a mechanism using PL/pgSQL to wrap the query export process with additional functionality, enabling monitoring the job status, capturing errors, and gathering metadata related to the export. The mechanism would trigger the transfer of data from S3 to the customer's designated sink.
- The solution implemented by Aqua using Step Functions and aws_s3.query_export_to_s3 function offers numerous benefits such as scalability, cost-effectiveness, automation, and enhanced security operations.
- The integration of Step Functions and the aws_s3.query_export_to_s3 function has empowered Aqua to address their query data export challenges effectively, and benefit from streamlined data export processes, enabling scalable and cost-effective data analysis for enhanced business operations.
- If you would like to learn more about the aws_s3.query_export_to_s3 function, please see the user guide documentation.
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