A naukri.com initiative
Databases
VentureBeat
1M
94
Image Credit: VentureBeat
CockroachDB’s distributed vector indexing tackles the looming AI data explosion enterprises aren’t ready for
- Cockroach Labs' latest update focuses on distributed vector indexing and agentic AI in distributed SQL scale, promising a 41% efficiency gain and core database improvements.
- With a decade-long reputation for resilience, CockroachDB emphasizes survival capabilities aimed to meet mission-critical needs, especially in the AI era.
- The introduction of vector-capable databases for AI systems has become commonplace in 2025, yet distributed SQL remains crucial for large-scale deployments.
- CockroachDB's C-SPANN vector index utilizes the SPANN algorithm to handle billions of vectors across a distributed system.
- The index is nested within existing tables, enabling efficient similarity searches at scale by creating a hierarchical partition structure.
- Security features in CockroachDB 25.2 include row-level security and configurable cipher suites to address regulatory requirements and enhance data protection.
- Nearly 80% of technology leaders feel unprepared for new regulations, emphasizing the growing concern over financial impacts of outages due to security vulnerabilities.
- The rise of AI-driven workloads introduces 'operational big data,' demanding real-time performance and consistency for mission-critical applications.
- Efficiency improvements in CockroachDB 25.2, like generic query plans and buffered writes, enhance database performance and optimize query execution.
- Leaders in AI adoption must consider investing in distributed database architectures to handle the anticipated data traffic growth from agentic AI.
Read Full Article
5 Likes
Analyticsindiamag
1M
143
Image Credit: Analyticsindiamag
Why CarDekho Replaced SAP with Oracle Ahead of IPO
- CarDekho is focusing on technology-driven financial transformation as it prepares for its IPO, relying on Oracle's cloud-based ERP solution for streamlined financial operations.
- The move from an outdated SAP system to Oracle was driven by the need for enhanced capabilities, scalability, and cost savings, with Oracle's cloud model offering advantages like reduced operational costs and easier updates.
- Implementation of Oracle ERP, EPM, and SCM modules aimed to consolidate systems, reduce reliance on Excel, and improve business processes at CarDekho.
- CarDekho is among many companies transitioning from SAP to Oracle for better efficiency, cost savings, and streamlined operations.
- The switch to Oracle has enabled CarDekho to standardize operations, gain real-time insights, and prepare for further value with Oracle's EPM module.
- CarDekho is looking towards AI adoption for automating standard processes and leveraging Oracle's AI capabilities for improved efficiency.
- The move towards cloud-first platforms like Oracle showcases how finance teams are embracing technology for speed, consistency, and scalability, setting the stage for IPOs and future tech adoption.
Read Full Article
8 Likes
Medium
1M
400
Image Credit: Medium
When You Use Indexing, How Indexing Affects Query Execution and MySQL’s Internal Processes?
- Indexing is crucial for fast data retrieval in relational databases like MySQL.
- MySQL heavily relies on indexing to optimize queries, but a lack of understanding can lead to performance issues.
- This article explores MySQL's indexing mechanisms, including clustered and secondary indexes, B+ tree data structures, and query execution processes.
- Indexes in MySQL, particularly B+ trees, allow for quick locating of rows based on conditions, improving query efficiency.
Read Full Article
24 Likes
Dbi-Services
1M
220
Guidance for a SQL Server backup strategy in AWS EC2
- S3 storage is a reliable option to store backups long term in AWS EC2.
- The preferred ways to backup SQL Server in AWS EC2 include using URLs or Storage Gateway with File Gateway for S3 replication.
- Backup retention strategies are crucial for data longevity, with options like optimizing storage class and defining retention periods.
- S3 lifecycle policies are set up to manage retention, transitioning objects to appropriate storage classes and deleting them after specified periods.
- Archiving backups efficiently requires custom scripts to move backups between different prefixes in S3, scheduled using tools like AWS Lambda or SQL Server Agent.
- Accessing S3 from EC2 involves setting up IAM roles and securing temporary AWS credentials for read/write access only.
- Retrieving backups for archiving is facilitated by tools like dbatools PowerShell module, aiding in selecting and processing backups based on defined criteria.
- Archiving backups involves constructing source and destination keys in S3, and copying backups to designated prefixes using tools like Copy-S3Object.
- Implementing a SQL Server backup strategy in AWS EC2 involves demystifying concepts, defining retention policies, and using tools like dbatools for efficient archiving.
- Similar approaches can be applied to other RDBMS in EC2, with considerations for access restrictions and compression settings in SQL Server backups.
Read Full Article
13 Likes
Discover more
- Programming News
- Software News
- Web Design
- Devops News
- Open Source News
- Cloud News
- Product Management News
- Operating Systems News
- Agile Methodology News
- Computer Engineering
- Startup News
- Cryptocurrency News
- Technology News
- Blockchain News
- Data Science News
- AR News
- Apple News
- Cyber Security News
- Leadership News
- Gaming News
- Automobiles News
Dev
1M
260
Image Credit: Dev
Understanding and Inspecting Indexes in MySQL: A Comprehensive Guide
- Indexes in MySQL help improve performance by reducing the data volume scanned for queries.
- Inspecting and understanding indexes is crucial for database optimization and maintenance.
- MySQL supports primary, unique, full-text, spatial, composite, and prefix indexes.
- SHOW INDEX commands allow inspecting defined keys and their attributes in MySQL.
- Understanding index structures, cardinality, and composite designs is essential for efficient querying.
- Removing redundant indexes and optimizing composite designs can improve query performance.
- Evaluation of existing indexes before adding new ones is important for query efficiency.
- The information_schema.STATISTICS table provides a centralized view of all index metadata in a schema.
- Automation tools like Releem can help track query performance and suggest index improvements.
- Continuous monitoring and adjustment of indexes are essential for maintaining database performance.
Read Full Article
15 Likes
Amazon
1M
256
Image Credit: Amazon
Streamline code conversion and testing from Microsoft SQL Server and Oracle to PostgreSQL with Amazon Bedrock
- Organizations are migrating from legacy database engines like Microsoft SQL Server and Oracle to PostgreSQL to reduce costs and enhance flexibility.
- Amazon Bedrock, a generative AI platform, helps simplify and accelerate code conversion tasks for migrations.
- Challenges in database migration include schema conversion, business logic transformation, data migration, application changes, and performance tuning.
- Amazon Bedrock automates schema and code conversion, AI-driven data transformation, code compatibility insights, and intelligent testing.
- Prompt engineering with AI models like Anthropic’s Claude in Amazon Bedrock enhances code conversion accuracy and efficiency.
- Example code conversion from Microsoft SQL Server to PostgreSQL is demonstrated using Amazon Bedrock.
- Amazon Bedrock provides code coverage analysis for the generated test cases and ensures comprehensive testing for the converted code.
- Validation scripts and test data are offered by Amazon Bedrock to test the converted function in your PostgreSQL environment.
- Automating test cases with Amazon Bedrock APIs can streamline code validation, performance assessment, and business logic optimization.
- AWS tools and services, including Amazon Bedrock and Database Migration Service, accelerate database modernization and migration tasks.
- Authors Viswanatha Shastry Medipalli, Jose Amado-Blanco, and Swanand Kshirsagar share expertise in database migrations and cloud architecture.
Read Full Article
15 Likes
Amazon
1M
410
Image Credit: Amazon
Implement prescription validation using Amazon Bedrock and Amazon DynamoDB
- Healthcare providers are now using generative AI capabilities to search patient records and verify medication safety without complex queries.
- An AI agent created with Amazon Bedrock and DynamoDB helps healthcare providers identify potential drug interactions in new prescriptions.
- The solution leverages the speed of DynamoDB and natural language processing of Amazon Bedrock to access medication records and interactions.
- DynamoDB data model allows quick lookups of patient records and medications with single-digit millisecond performance crucial in healthcare.
- Each patient's medication record in DynamoDB contains interaction checks, enabling quick access and comprehensive history.
- A prescription validation system is implemented, enabling healthcare providers to check drug interactions through conversations.
- The solution uses a single-table design in DynamoDB for efficient data retrieval.
- Amazon Bedrock knowledge base is utilized for medication classifications and interaction effects.
- The implementation provides Lambda functions for querying patient records and updating interactions in DynamoDB.
- The solution presents performance metrics, scaling guidance, cost breakdown, security considerations, and troubleshooting tips.
Read Full Article
24 Likes
Hackernoon
1M
225
Image Credit: Hackernoon
The Marketing Data Cleaning Query Cookbook
- In the world of Agentic AI, data quality is crucial as autonomous systems drive marketing decisions based on CRM and analytics data.
- Gartner reports that poor data quality costs organizations an average of $12.9 million annually, affecting efficiency and decision-making.
- The article offers a SQL query cookbook for marketers to clean and enhance data using SQL Server Management Studio.
- Marketers can now describe data issues in plain English, generate SQL queries, and run them without advanced technical support.
- Queries include tasks like fixing name capitalization, identifying and fixing suspicious or swapped names, building full names, and trimming extra spaces.
- Additionally, the cookbook covers fuzzy matching, standardizing text, dealing with missing data, and avoiding duplicate data using SQL queries.
- It also addresses out-of-range values, contradictory data, invalid emails, identifying internal or test contacts, and splitting data into multiple columns.
- Lastly, the article suggests training GPT models on specific schemas for more accurate SQL assistance.
- Clean data is emphasized as the key to successful AI implementation in marketing, facilitated by SQL queries and ChatGPT.
- Marketers are encouraged to leverage the cookbook, engage with ChatGPT, and enhance their data management skills for efficient AI utilization.
Read Full Article
13 Likes
Dev
1M
13
Image Credit: Dev
Stop Forcing Time Windows on Bursty User Behavior - Try This Instead
- User activity often occurs in bursts, challenging traditional fixed-time windows in analytics.
- Session Windows offer a smarter approach by grouping events based on activity patterns.
- Benefits include flexible event grouping, behavior-driven analytics, simplified querying, and enhanced insight.
- Session Windows group events close in time, considering a gap duration to define sessions.
- Recommended for modeling user behavior, IoT data, anomaly detection, and financial market activities.
- RisingWave employs the 'SESSION' window function frame type for session window semantics.
- SQL query examples showcase defining session windows, computing session boundaries, and aggregating data.
- Aggregating data within session windows aids in calculating metrics per session group.
- Session windows in RisingWave provide behavior-driven insights into real-world event streams' irregular nature.
- Explore RisingWave Cloud for a managed experience or the open-source version for self-running session windows.
Read Full Article
Like
Medium
1M
108
Image Credit: Medium
AI Can Write SQL — So What Happens to Data Engineers?
- AI tools like ChatGPT are becoming proficient at writing SQL queries quickly.
- There is a concern among data engineers that AI may replace their jobs due to this capability.
- Although AI can write SQL, it cannot replicate the role of a skilled data engineer anytime soon.
- SQL is just one aspect of a data engineer's job, and their expertise involves much more than just writing queries.
Read Full Article
6 Likes
Amazon
1M
356
Image Credit: Amazon
Build a multi-Region session store with Amazon ElastiCache for Valkey Global Datastore
- The article discusses building a multi-Region session store with Amazon ElastiCache for Valkey Global Datastore.
- The solution offers a unified database caching layer for application servers and secure cross-Region replication.
- ElastiCache Global Datastore allows writing to one cluster and reading from two other cross-Region replica clusters.
- It enables low-latency reads and disaster recovery across Regions.
- The article explains how to evolve a caching architecture from a single Region to a multi-Region setup.
- This transition involves duplicating infrastructure to a second Region and utilizing AWS Global Accelerator for optimal connectivity.
- Challenges of a cross-Region caching layer are discussed focusing on dataset sharing between Regions.
- To address these challenges, adopting the ElastiCache Global Datastore feature is recommended.
- The article provides insights on implementing ElastiCache for Valkey Global Datastore for secure cross-Region replication.
- It includes detailed steps on configuring VPC peering or using AWS Transit Gateway for cross-Region connectivity.
- An automation solution using AWS Lambda and Route 53 is introduced for updating DNS records upon global datastore failover.
Read Full Article
21 Likes
Dev
1M
388
Image Credit: Dev
The Best Tools to Design Database Schemas Visually in 2025
- Visual database tools are essential for designing schemas, collaborating, and documenting work, especially with growing complexity and team collaborations.
- DbSchema stands out for its SQL and NoSQL support, visual schema diagrams, offline work capability, Git version control, and ability to generate sample data.
- DBeaver, an open-source tool, provides good ERD viewer, connects to many database systems, and offers useful SQL editor, although obtaining a trial license can be cumbersome.
- DataGrip by JetBrains targets developers with smart autocomplete and refactoring for SQL, diagram view, support for various SQL engines, and built-in database inspection tools.
- dbForge Studio excels in solid diagramming tools, comparison features, automation options, but has limited support for PostgreSQL and is more focused on SQL Server and MySQL.
- Vertabelo, a web-based tool, emphasizes clean design, early planning, logical and physical design views, version tracking, and multi-user commenting, making it suitable for planning phases.
- MySQL Workbench is ideal for MySQL users, with features like schema diagramming, forward and reverse engineering, table relationship mapping, but lacks multi-database flexibility.
- Navicat offers visual ERD creation, data sync, cloud integration, and supports various SQL databases, but creating diagrams may involve additional steps compared to other tools.
- Tools like Toad Edge, SQLDBM, and HeidiSQL cater to specific needs like MySQL and PostgreSQL support, online collaboration, and lightweight tasks, each with its distinct strengths and limitations.
- SQLDBM, a clean and modern web-based tool, offers a good visual design, online team collaboration, GitHub integration, but certain advanced features are restricted to paid plans.
- HeidiSQL, a free and simple desktop tool, is efficient for quick database exploration and queries, supporting key databases like MariaDB, MySQL, SQL Server, and PostgreSQL, despite its basic interface and limited features.
Read Full Article
23 Likes
Siliconangle
1M
447
Image Credit: Siliconangle
IBM’s agentic strategy brings generative AI down to earth and into production
- IBM is focusing on a strategy called 'agentic strategy' to scale generative AI ambitions effectively, emphasizing execution oversight and governance.
- This strategy aims to balance creative momentum with practical operational discipline, bridging the gap between content creation and production-related challenges.
- Bruno Aziza, IBM's group vice president for data, emphasizes the importance of managing costs and governance alongside content engineering and engagement.
- IBM's agentic strategy is about agents working across apps, clouds, and workflows to drive actual value and focuses on both better engagement and efficient production management.
- IBM is promoting a federated framework for integration, enabling data, agents, and business logic to move seamlessly across various silos, clouds, and tools.
- The company offers prebuilt agents connected to numerous applications, allowing organizations to start fast, customize as needed, and scale confidently in hybrid and multi-cloud environments.
- IBM is aiming to avoid hard-coded processes and rigid workflows by enabling dynamic agent behavior through its watsonx Orchestrate product for flexible and adaptive strategies.
- The focus of IBM's approach goes beyond the excitement of generative AI to address operational challenges like scale, accountability, cost, and governance.
- IBM emphasizes the importance of transformation, automation where needed, elimination of unnecessary processes, and a modular mindset to optimize use cases.
- IBM's open approach to operationalizing agents at scale emphasizes its collaboration with companies like Box, Oracle, and Salesforce in production environments.
Read Full Article
26 Likes
Medium
1M
384
Image Credit: Medium
1Z0–830 Sample Questions for Java SE 21 Developer Certification Exam (with Resources)
- The article provides sample questions for the 1Z0–830 exam or Java 21 certification exam and offers a discount coupon for a related Udemy course.
- Java 21 Certification Exam (1Z0–830) is considered challenging with long, intricate questions and multiple options.
- The exam duration has been extended to 120 minutes for 50 questions, requiring candidates to solve questions efficiently.
- It is crucial to practice sample questions and take mock tests to enhance speed and accuracy in preparation for the exam.
- The Udemy course '1Z0–830 Java SE 21 Developer Professional Exam Practice Test' contains over 266 questions related to the certification.
- Sample questions from the article involve coding scenarios and multiple-choice queries to test Java knowledge.
- Readers are encouraged to attempt the sample questions provided in the article and can avail the course at a discounted price using a coupon code.
- The article emphasizes the importance of first-time success in the Java certification exam due to its cost and difficulty level.
- The author offers insights into preparing for Java SE 17 certification as well and recommends relevant Udemy resources for exam readiness.
- To access additional Java 21 exam-related articles and resources, readers are directed to explore further content shared by the author.
Read Full Article
23 Likes
Dev
1M
117
Image Credit: Dev
Why Cloud Infrastructure Matters
- Cloud infrastructure is essential for delivering computing resources over the internet, allowing for scalability, efficiency, and cost-effectiveness.
- The shift to cloud infrastructure is driven by the need for faster development, remote work capabilities, scalability, and resilience in the face of disasters.
- Oracle Cloud Infrastructure (OCI) offers robust solutions for running databases and enterprise workloads, showcasing the diverse approaches of cloud providers in addressing similar challenges.
- The field of cloud engineering is dynamic and continually evolving, presenting opportunities to explore new tools, enhance CI/CD pipelines, and gain a deeper understanding of cloud systems.
Read Full Article
7 Likes
For uninterrupted reading, download the app