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Simplify SQL Server Integration with ADONet Sql Server Tools for .NET

ADONet Sql Server Tools for .NET is a powerful library that simplifies SQL Server integration.
Key features include executing queries, running stored procedures, handling transactions, and performing bulk insertions.
The library provides functionalities like query execution, stored procedure execution, bulk insertions, transaction management, schema management, and JSON export.
You can easily add ADONet Sql Server Tools via NuGet for Visual Studio or .NET CLI.

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Dev

234

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How PostgreSQL Powers the Future of Data-Driven Applications

PostgreSQL is becoming the backbone of modern data-driven applications due to its advanced features and scalability.
It is widely used in e-commerce for managing product catalogs, customer data, and ensuring transactional integrity.
PostgreSQL is also popular in analytics and data warehousing, geospatial applications, and microservices architectures.
Continuous community-driven updates, cloud compatibility, and strong security and reliability features make PostgreSQL future-proof.

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Dev

353

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PostgreSQL vs. Other Databases: Why It’s the Top Choice for Modern Applications

PostgreSQL stands out among other databases due to its advanced features, scalability, and open-source nature.
Its unmatched extensibility allows for flexibility and compatibility with various industries.
PostgreSQL ensures data consistency and integrity, making it reliable for critical applications.
With advanced query capabilities and support for structured and unstructured data, PostgreSQL is a powerhouse for complex queries and hybrid data management.

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Siliconangle

348

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The yellow brick road to agentic AI

Agentic AI requires a strong data foundation built upon cloud-like scalability, a unified metadata model, and harmonized data and business process logic.
Agentic automation is expected to become a reality within the next decade and will require a reinvention of the software stack.
The enterprise software stack is changing, and there is a shift from control at the database layer toward the governance catalog using operational metadata.
To achieve true automation in agentic AI, it's crucial to have harmonized process logic that enables agents to communicate across the entire customer journey.
The future of agentic AI will involve harmonizing end-to-end business processes to ensure that agents and humans collaborate effectively and share a common understanding.
Data platforms such as Snowflake and Databricks, organizations such as Palantir and Salesforce, and vendors like Google, Microsoft, and UiPath Inc. are leading the way to agentic automation.
The process of achieving agentic automation involves creating new infrastructure layers, incorporating genuine harmonization, and avoiding the trap of superficial bolt-ons.
Achieving the vision of agentic automation will take time, persistence, and focusing on the pieces that do not yet exist in the enterprise software stack.
The opportunity is to reinvent the application stack and not perpetuate the status quo by bolting agents onto existing legacy architectures.
The agentic future that delivers a 10x productivity gain hinges on harmonizing end-to-end business processes and ensuring that agents and humans collaborate effectively and share a common understanding.

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Dev

390

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SQL Basic Question for interview

How do you display all columns and rows from the customers table?
Write a query to retrieve only the customerName and phone from the customers table.
How do you list all rows where country is 'USA' in the customers table?
Write a query to find all products in the products table with a buyPrice less than 50.

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Sanjeevvellaisamydba

How to Verify Standby Database Sync with Primary Database in 19c

To verify if the standby database is in sync with the primary database in Oracle 19c, follow these steps:
1. Compare Sequence Numbers: Check the latest sequence number generated on the primary database and the latest sequence number applied on the standby database.
2. Check for Gaps: Ensure there are no missing sequence numbers on the standby database.
To verify the status of the primary and standby databases:
- On the primary server, log in and run the SQL query to check the status.
- On the standby server, log in and execute the same SQL query.
To verify the gap of the standby:
- Find the last primary sequence generated.
- Identify the last standby sequence received.
- Determine the last standby sequence applied.
To identify the hostname of the DR server from the primary database:
- Run the provided query to display the DR server's hostname.

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Dev

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Front-End to Full-Stack Journey: SQL & SQLite

The author shares their progress on their front-end to full-stack journey.
They completed Code Academy's free courses on backend programming and Node.js fundamentals.
They recommend Code Academy for its structured learning approach.
The author learned about Express and middleware in order to connect a React component to a SQL database.

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Cloudblog

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Spanner in 2024: A year of innovation

Google's Spanner, which powers Gmail, YouTube, and Google Photos, evolved in 2024 into a multi-model database with interoperable capabilities, cost-effective pricing with multiple editions, and a powerful Cassandra proxy adapter.
Spanner's multi-model intelligently consolidates databases and eliminates data movement for a true ZeroETL experience, while its price-performance lets you scale with no cliffs.
The Cassandra to Spanner Proxy Adapter makes it easier than ever to migrate workloads to Spanner with minimal changes, and vector support allows for similarity searches on vector embeddings.
New integrations between Spanner and BigQuery improve decision-making, and dual-region configurations and geo-partitioning improve availability and governance controls. Incremental and scheduled backups also make data backup easier and cheaper.
Google Cloud also improved Spanner's developer experience by allowing protobuf values to be managed in Spanner, adding support for end-to-end tracing, and introducing database center support.
Google intends to continue adding exciting capabilities to Spanner in 2025.
You can try Spanner for free for 90-days or for as little as $65 USD/month for a production-ready instance.

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Cloudblog

300

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Database Center: Now with support for Bigtable, Firestore, and Memorystore

Google Cloud's Database Center now supports Bigtable, Memorystore, and Firestore databases.
Database Center offers a comprehensive view of the entire database fleet, enabling easier management.
Proactive monitoring and issue-resolution capabilities are expanded to support Bigtable and Firestore databases.
AI-powered assistance through Gemini interface helps optimize the database fleet.

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Dev

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Migrating from Azure Database for PostgreSQL to Neon

Azure Database for PostgreSQL can now be migrated easily to Neon, which offers serverless architecture, auto-scaling capabilities, and actual data storage billing. The migration process can be done using the utility tool pg_dump and pg_restore that can efficiently backup and restore a Postgres database. An accessible step-by-step guide is available for developers. Robust solutions that address scalability, complex dependencies, or strict security requirements are also available. Post-migration verification guarantees the integrity of data moved between the databases.
Azure Database for PostgreSQL offers the great Postgres features that many developers are familiar with while managing infrastructure for them. However, the fixed capacity that requires some overprovisioning could lead to heightened costs. Neon offers serverless architecture, which automatically scales compute resources to zero when idle and allows billing based on actual data size instead of provisioned capacity.
The step-by-step guide on how to easily migrate from Azure Database for PostgreSQL to Neon can be done by the tool pg_dump and pg_restore backed-up and restored a Postgres database that maintains dependencies and referential integrity. This process remains the most reliable way of migration, however, other options exist as well. For example, logical replication can be used for migrations of databases in the terabyte range.
Complex dependencies in Postgres that often create circular migration challenges include, for example, cross-schema references and custom extensions that don't have direct equivalents in the target system. A three-phase migration approach addresses the issue: pre-schema, schema, and post-schema. Security and Compliance-related challenges can be addressed by implementing a security-first migration pattern that includes tasks such as export and map of all roles and permissions.
The migration from Azure Database for PostgreSQL to Neon might take some planning, but the long-term advantages make it worthwhile. Neon's serverless architecture offers automatic scaling, capacity that matches real usage, and smarter cost efficiency.
Developers using Postgres features through Azure Database for PostgreSQL have the option to migrate to Neon's serverless architecture. The process is smooth, and robust solutions that address potential scalability, complex dependency, and strict security-related issues are also available. The migration options include logical replication and a three-stage migration approach method.
The method to migrate easily from Azure Database for PostgreSQL to Neon is using the utility tool pg_dump and pg_restore. Both backup and restore operations handle dependencies and referential integrity efficiently. Post-migration verification safeguards guarantee data integrity between the databases.
With Neon, one can enjoy Postgres features with the added benefits of serverless architecture, auto-scaling capabilities, and smarter cost efficiency. Migrating from Azure Database for PostgreSQL to Neon is an advantage worth taking, considering its familiar Postgres experience and convenience.
Large data volumes, complex dependencies, and compliance and security requirements create migration challenges when moving from Azure Database for PostgreSQL to Neon. Logical replication and a three-phase migration approach are viable solutions. A security-first migration pattern that addresses security policies, compliance requirements, audit logging, is a good practice to prevent unauthorized access during migration.
The step-by-step guide available for developers who need to migrate from Azure Database for PostgreSQL to Neon involves using the utility tool pg_dump and pg_restore, backed-up, and restored the Postgres database. The method maintains referential integrity and handles dependencies efficiently. Migrating to Neon offers smarter cost efficiency, serverless architecture, and automatic scaling capacity, providing a better user experience.
The migration from Azure Database for PostgreSQL to Neon opens up serverless architecture opportunities that are not available before. While the migration process requires careful planning, the long-term advantages are promising- like smarter cost efficiency, automatic scaling capacity, and better user experience.

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Dbi-Services

138

FreeBSD basics – 8 – Running Linux Containers

FreeBSD users now have an additional option to install Podman, a popular Linux container-building and running tool, which is now available for FreeBSD 14.2.
Podman can restart containers after a host is rebooted.
To enable this on FreeBSD, mount the fdescfs file system and create a container network configuration.
Users can also run many Linux container images using FreeBSD’s Linux emulation feature.
The FreeBSD port of the Podman container engine is experimental and should only be used for evaluation and testing purposes.
Podman can be installed using a binary package or from the ports collection.
To start containers with a restart policy, use the command: $ sudo podman run -d --restart=always myimage.
To search for container images using Podman, users first need to define container registries by adding a registry to “/usr/local/etc/containers/registries.conf”.
Once a registry is defined, users can search for container images using Podman and run them on FreeBSD.
Running Linux containers on FreeBSD gives users another option in the FreeBSD world and expands the choices for where to run containers.

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Medium

266

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From Prototype to Production: Enhancing LLM Accuracy

This article discusses how to measure and improve accuracy for an SQL agent built using LLM model and SQL database. Starting with a prototype, the article explores methods to measure accuracy and improve it using self-reflection and retrieval-augmented generation (RAG) techniques.
The LLM model used in this project is Llama 3.1 8B from Meta, and the SQL database is ClickHouse. After building the prototype, the author creates a “golden” evaluation set of questions and correct answers to compare the model's output with them.
The author discusses the nuances of evaluating accuracy and scoring the generated results of queries. Then, the article explores self-reflection and RAG techniques to improve accuracy.
The article also discusses the usage of Chroma database as a local vector storage with OpenAI embeddings to find chunks that are similar to the query for RAG.
Finally, after combining self-reflection and RAG approaches, the author achieved 70% accuracy, which can be further improved using fine-tuning technique.

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Amazon

155

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Transition from AWS DMS to zero-ETL to simplify real-time data integration with Amazon Redshift

Organizations are shifting to real-time analytics to gain insights and maintain a competitive edge. AWS DMS was designed to help migrate workloads between databases, but you can also use it for ongoing change data capture (CDC) replication. Zero-ETL integrations for Amazon Redshift automate data movement, eliminating the need for traditional ETL pipelines.
Advantages of zero-ETL integration include reduced costs, minimized impact on source and destination databases, reduced end-to-end latency, simplified replication process, and streamlined operations.
Make sure to review considerations before using zero-ETL integrations with Amazon Redshift. Not all configurations and use cases are best supported by zero-ETL integration.
To transition from AWS DMS to zero-ETL integration, first set up zero-ETL integration and validate it. Migrate data consumer tools connections to zero-ETL integration database, update or create views, and then disable AWS DMS replication.
Zero-ETL integration for Amazon Redshift is a more efficient and cost-effective approach for CDC and ETL within AWS. AWS DMS remains the recommended service for migrating data into AWS databases.

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9 Likes

VoltDB

180

Image Credit: VoltDB

The Data Trends That Defined 2024

Streaming data platforms have been a key trend in 2024, allowing real-time decision-making in various industries.
AI-powered data analytics have evolved, moving beyond predictive models to include prescriptive analytics.
Privacy-enhancing technologies (PETs) have gained adoption to ensure data privacy while allowing analysis.
The data mesh paradigm has gained traction, decentralizing data ownership and improving accessibility and collaboration.

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Dev

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MySQL Memory Usage: A Guide to Optimization

MySQL dynamically manages memory across several areas to process queries, handle connections, and optimize performance.
The three primary areas of memory usage in MySQL are global buffers, connection buffers, and operating system memory usage.
Common reasons for memory spikes in MySQL include high traffic with large connection buffers, complex queries, oversized InnoDB buffer pool, large temporary tables, and inefficient indexing.
Best practices for controlling MySQL memory usage include setting limits on global buffers, adjusting connection buffer sizes, fine-tuning table caches, controlling thread cache and connection limits, tracking temporary table usage, using MySQL memory calculator, and monitoring query performance.

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