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HRKatha

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Oracle to train 5 lakh in AI, ML, GenAI, cloud computing

Oracle plans to train 5 lakh Indian youth and women in AI, ML, GenAI, cloud computing and other digital technologies over the next three years.
The training is part of the Project Vidya initiative, in partnership with the Tamil Nadu Skill Development Corporation (TNSDC) and the National Skill Development Corporation (NSDC).
Oracle University will provide training modules and certifications via Oracle MyLearn, aiming to make participants employable in technology-driven jobs.
Oracle seeks collaboration with state governments and skilling organizations to expand the program across India.

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Dbi-Services

402

PostgreSQL 18: Introduce autovacuum_vacuum_max_threshold

PostgreSQL 18 introduces a new parameter called "autovacuum_vacuum_max_threshold" to improve autovacuum configuration.
The default autovacuum behavior in PostgreSQL is triggered when 20% of the table and 50 tuples change.
By adjusting "autovacuum_vacuum_threshold" and "autovacuum_vacuum_scale_factor", the triggering conditions for autovacuum can be customized.
With the new "autovacuum_vacuum_max_threshold" parameter, PostgreSQL will handle specific cases automatically.

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Mysql

329

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MySQL Router bootstrap & “conf-set-option”

The MySQL Router documentation provides a handy option - --conf-set-option - to add parametrization for routers.
The --bootstrap command is used to bootstrap MySQL Router with specified options.
The conf-set-option allows customization of parameters such as error quarantine threshold, connection pool options, and maximum connect errors.
When using ClusterSets, the router needs to be rebootstraped with force.

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Cloudblog

178

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Rightsize your Memorystore for Redis Clusters with open-source Autoscaler

Google has launched an open source Redis Platform autoscaler on GitHub.
The new offering allows software developers and DevOps workers to automate the process of scaling Redis data operations up or down, based on changes in utilisation, freeing up staff time and reducing costs.
The autoscaler is flexible in deployment, with many configuration options possible, and writes its actions and reasoning to Cloud Logging to aid in tracking and auditing.
The scalable, open-source system is designed to help customers with batch workloads or other known bursts in Redis data usage, providing them with the capacity they need to carry out processes as efficiently as possible.
The platform can be customised to include memory configurations to sit alongside the CPU utilisation scaling options. It also provides administrators with capacity to proactively monitor the changes in Redis platform utilisation daily.
When used in conjunction with App Engine, the Redis autoscaler can adjust the capacity of the delivered system instantly, removing the need for engineers to manually intervene as data usage patterns change over time.
By automating Redis scaling in relation to system utilisation patterns, developers can focus on other IT deployment and development considerations.
The autoscaler writes a summary of its actions and recommendations to Cloud Logging, as well as passing metrics on to Cloud Monitoring for an overview of system activities at all times.
The OSS Autoscaler comes with a Terraform configuration to get you started, which can be integrated into your codebase for production deployments.
If there are additional features you would like to see in the Autoscaler — or would like to contribute to it yourself — please don’t hesitate to raise an issue via the GitHub issues page.

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Cloudblog

292

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Announcing public beta of Gen AI Toolbox for Databases

Gen AI Toolbox for Databases is an open-source server that empowers application developers to connect production-grade, agent-based generative AI (gen AI) applications to databases.
It streamlines the creation, deployment, and management of sophisticated gen AI tools capable of querying databases with secure access, robust observability, scalability, and comprehensive manageability.
It provides connectivity to popular open-source databases such as PostgreSQL, MySQL, as well as Google’s industry-leading Cloud Databases like AlloyDB, Spanner, and Cloud SQL for SQL Server.
Gen AI Toolbox for Databases improves how gen AI tools interact with data, addressing common challenges in gen AI tool management.
Toolbox comprises two components: a server specifying the tools for application use, and a client interacting with this server to load these tools onto orchestration frameworks.
Toolbox offers features that provide better manageability, security and observability for AI Agents.
The integration of Gen AI Toolbox for Databases with the LangChain ecosystem is a boon for all developers.
Gen AI Toolbox for Databases simplifies gen AI tool development and deployment by automating the entire lifecycle.
Here are some resources to get started: Github repo, Documentation.
Quickstart - How to get started running a LangGraph agent with Toolbox using Gemini on Vertex AI.

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Insider

255

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Meet Paula Hurd, the philanthropist dating Bill Gates who got a shoutout in his new book

Bill Gates has confirmed that he's dating philanthropist Paula Hurd following his divorce from Melinda French Gates.
Paula Hurd, the widow of late Oracle CEO Mark Hurd, has worked in sales and service leadership roles in her career.
Gates mentioned Paula Hurd in the acknowledgments section of his new book, 'Source Code.'
Bill Gates and Paula Hurd have been spotted together at various events such as the 2024 Breakthrough Prize Ceremony and the 2024 Paris Olympics.

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Dev

320

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Connection Pool in Backend Development: Basic Concept, Benefits, and Implementation

Connection pooling creates and manages a pool of database connections that can be used by applications, improving performance and better resource use.
Connection pooling avoids creating new connections every time it is needed, and allows applications to use existing connections and return them to the pool when finished.
Connection pooling has several benefits, including: better performance, resource savings, scalability, and load configuration.
The first time an application is run, it creates a certain number of connections to the database. This number is called Pool size.
When the application needs a connection to the database, it no longer creates a new connection, instead requests a connection from the pool.
Once an application has finished using a connection, it will not close the connection but return it to the pool to be used again by the next request.
Some common main parameters in connection pooling include: Pool Size, Minimum Connections, Idle Connection, Timeout, and Max lifetime.
The implementation of connection pooling can be done using Golang programming language.
The use of connection pooling is very important, and the right settings must be implemented to ensure optimal resource use.
The discussion above helps in understanding the basic concept, benefits, and implementation of Connection Pooling in Backend Development.

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Cloudblog

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Solve database bottlenecks faster with the latest query insights for Cloud SQL Enterprise Plus

Cloud SQL Enterprise Plus edition introduces new capabilities to query insights for optimized database performance.
Key features of query insights for Cloud SQL Enterprise Plus edition include fine-grained database metrics for root-cause analysis, control over active query executions, intelligent recommendations for dynamic workloads, and an AI-powered chat interface for troubleshooting.
With 30 days of telemetry data, users can analyze long-term query performance trends and make data-driven optimizations.
Additional features include wait events for diagnosing performance bottlenecks and index recommendations for enhancing query performance.

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Dev

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The Intersection of Data Science and Cloud Computing

The convergence of data science and cloud computing has transformed the approach of Indian businesses to data management. These technologies make data-driven decision-making much more accessible while offering scalability and cost efficiency. Cloud computing infrastructure supports data science activities on a larger scale, providing an unlimited amount of storage capacity and processing power. Cloud platforms allow companies to only pay for what they use and provide access to cutting-edge tools and technologies for organizations that need quick solutions to stay competitive. Cloud-based-data science modeling predicts future failures, optimizes supply chains, improves production processes, and provides real-time analytics.
Cloud computing makes real-time analytics possible, which is essential in e-commerce, healthcare, and the financial sector where real-time data is generated constantly, and timely decisions must be taken. Retailers access cloud-based data science tools to analyze consumer behavior, optimize sales strategies, and forecast demand. Healthcare institutions use predictive analytics to monitor patient health in near-real time. The modern requirement of combining both technologies creates a surge for individuals with expertise. A data science certification course in Thane offers an accessible foundation and hands-on experience with cloud platforms, forecast algorithms, and data visualization. One of the many benefits of such courses is the networking opportunity with industry experts and recruiters. Cloud-related data science is constantly changing; therefore, the students receive the latest technical exposure for work. The combined adoption of data science and cloud computing presents a promising future for businesses.

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Dev

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Schema Changes Are a Blind Spot

Schema changes and migrations can quickly spiral into chaos, leading to significant challenges.
Databases are prone to various types of failures, yet they often don’t receive the same rigorous testing as applications.
Robust automated tests are essential to proactively identify problems and minimize dependence on manual interventions.
Load testing is a common approach to uncover performance issues, but it has significant drawbacks.
Schema migrations are often overlooked in testing processes.
Database guardrails enable actionable insights that identify the root cause of issues.
Metis then projects these activities onto the production database to assess their safety before deployment.
Database guardrails are designed to proactively prevent issues, advance toward automated insights and resolutions, and integrate database-specific checks at every stage of the development process.
Modern solutions, like database guardrails, tackle these challenges head-on.
Metis revolutionizes database management by automatically identifying and addressing potential issues.

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Amazon

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Create a 360-degree master data management patient view solution using Amazon Neptune and generative AI

Healthcare professionals make decisions with only a subset of available patient records, which is why it's essential to consolidate all healthcare information into a 360-degree master data management patient view using Amazon Neptune and generative AI. Patient data is assembled from various sources like EHRs, lab reports, prescriptions, and medical histories into one location; this provides a complete and accurate view of a patient's health and can improve efficiency, reduce risk, and enable better clinical decisions. Natural language querying using generative AI provides a means for physicians and patients to query data effectively. Moreover, visualization software like Graph Explorer provides a means of navigating and interpreting recorded information in an easy-to-understand manner.
Master data management (MDM) is vital for integrating patient data from different sources accurately and consistently while maintaining a single source of truth. MDM also improves decision-making, streamlines workflows, and personalizes treatment plans. MDM represents a critical feature for meeting regulatory standards while enforcing security and privacy around data.
The advantages of building a 360-degree patient view-data-driven approach include the following: 1) enabling personalized care, 2) detecting potential health issues early, 3) improving patient engagement, 4) identifying barriers to care, and 5) supporting clinical decision-making. Enabling physicians to personalize care through risk scores and understand a person's medical history will predict potential health issues early.
Using Amazon Neptune and generative AI ensures that healthcare providers have access to an institution's large medical record data to further support analytics; you can identify trends, track drug utilization for specific medical conditions and more. Graph Explorer, a low-code, open-source tool that integrates with LPG and RDF graph models, allows physicians to explore a patient's medical history to understand how different physicians have treated them and identify connections.

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Medium

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Is MySQL still in demand?

MySQL offers various security features, such as access control and SSL encryption.
MySQL is open source and free for most applications, with MySQL Enterprise versions available for additional features.
MySQL has a simple setup process and is suitable for small to medium-sized applications and web applications.
MySQL is known for its speed, reliability, and ability to handle large amounts of data efficiently. Popular social media applications like YouTube, Facebook, and Twitter use MySQL for their backend services.

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Cloudblog

306

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How we improved GKE volume attachments for stateful applications by up to 80%

Google Kubernetes Engine has improved the speed at which Persistent Disks (PDs) are attached and detached by up to 80%.
Less latency means better performance for user workloads and interactions with storage.
The Google Compute Engine Persistent Disk (GCE PD) Container Storage Interface (CSI) Storage Plugin now allows seamless storage access for Kubernetes workloads, making it easier to manage Compute Engine PDs in a GKE cluster.
Tens to hundreds of PDs were being attached to VMs, creating longer downtime during software upgrades.
The solution was the introduction of operation merging in Compute Engine, which transparently parallelizes detach and attach operations and optimizes PD attachment and detachment processes.
The best part is no customer action is needed and they automatically benefit from 80% improvement in p99 latency for attach workloads and 60% for detach workloads.
The custom solution also introduces a tiered approach for workflow execution and decouples incoming attach and detach requests from the actual execution of volume attachments.
Incoming attachDisk/detachDisk requests aren't queued within the same database row as the target Compute Engine instance entity to minimize database contention.
The technology aims to allow large-scale stateful applications to run seamlessly on GKE clusters.
Further information is available on the Google Kubernetes Engine website.

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Dbi-Services

265

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SQL Server AlwaysOn – Failover does not work but everything is green on the cluster

Failover on a SQL Server AlwaysOn 2 nodes cluster does not work.
Failover Cluster Manager and SSMS show everything is green and synchronized.
Failover attempts result in error messages like 'The action 'Move' did not complete.'
Solution: Check the Advanced Policies of the cluster resources, particularly the 'Other Resources' and 'Server Name' checkboxes.

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Dev

146

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How to generate realistic test data in MySQL for QA environments

Test data is crucial for quality checks in software development.
Generating realistic and secure test data in MySQL is essential for controlled environments.
Using SQL and stored procedures, realistic test data can be generated in MySQL without external tools.
Through anonymization techniques, privacy and data security can be ensured during the test data generation process.

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