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Medium

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SQL for Data Science in 2025: Essential Queries Every Data Professional must Know

Structured Query Language (SQL) is a key skill in data science for accessing and manipulating data.
This article explores the essential SQL queries every data professional should know.
SQL allows users to create, read, update, and delete data from databases.
Data scientists consider SQL mandatory for efficient data manipulation and handling in projects.

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Dev

198

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Prevent SQL Injection in RESTful APIs: A Comprehensive Guide

SQL Injection (SQLi) remains one of the most prevalent vulnerabilities in web applications, targeting RESTful APIs to extract sensitive information or compromise systems.
SQL Injection occurs when malicious input is executed as part of a SQL query. In RESTful APIs, attackers exploit vulnerable endpoints to bypass authentication, retrieve sensitive data, or corrupt databases.
Preventing SQL Injection in RESTful APIs can be done through techniques like using parameterized queries, validating user input, and implementing API security best practices.
To protect against SQL Injection vulnerabilities, you can leverage the Free Website Security Checker Tool to assess your APIs and secure your application before attackers exploit any loopholes.

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Dev

260

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EXPLAIN PLAN & DBMS_PROFILER Explanation

EXPLAIN PLAN is used before the query is executed to provide an execution plan for how Oracle intends to run the SQL statement.
It helps identify potential inefficiencies and optimize the query before execution.
DBMS_PROFILER is used after the execution of PL/SQL code to collect performance data like the time spent on each line of code and number of times each statement was executed.
It helps identify bottlenecks and performance issues in PL/SQL logic and optimize the code.

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Dev

220

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Table functions and Pipelined functions in PLSQL | Best Explanation

A table function is a user-defined function that returns a collection (e.g., a table or an array) that can be queried like a table in SQL.
Syntax of a table function:
CREATE OR REPLACE FUNCTION function_name RETURN collection_type IS BEGIN RETURN result_collection; END;
A pipelined function is a specialized table function that returns rows incrementally (one by one or in small batches) instead of processing and returning the entire collection at once.

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Siliconangle

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Cloud market share shows vendors eyeing a $1T opportunity

The cloud market is projected to surpass $1tn in revenue in 2025, including IaaS, PaaS, SaaS and professional services.
Enterprise Technology Research surveyed 1,775 IT decision makers and found Microsoft and AWS tend to dominate cloud spending patterns.
Our expanded view of the cloud market includes IBM and Oracle as well as other Chinese providers, focusing on IaaS, PaaS, SaaS and professional services.
IaaS is projected to approach $207 billion in 2024 with AWS, Azure, GCP and Alibaba taking more than 70% market share.
PaaS includes core database and AI services and is projected to exceed $80bn this year.
Tencent Holdings Ltd. and Huawei Technologies Co. are major cloud players in China and APAC regions.
SaaS and services markets are highly fragmented and non-hyperscalers capture substantial revenue.
The report is based on top-down and bottom-up methodologies with varying degrees of confidence and interpretation.
Overall, the cloud market is still growing rapidly and AWS, Microsoft and Google remain dominant players.

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Marktechpost

328

UC Riverside Researchers Propose the Pkd-tree (Parallel kd-tree): A Parallel kd-tree that is Efficient both in Theory and in Practice

UC Riverside researchers propose the Pkd-tree (Parallel kd-tree), an innovative data structure that aims to address challenges in managing high-dimensional datasets.
The Pkd-tree introduces efficient parallelism for parallel construction, batch updates, and various query types, making it highly performant for large-scale multi-dimensional data.
The Pkd-tree optimizes kd-tree construction and update mechanisms, achieving faster construction and update times while maintaining query efficiency.
The Pkd-tree is beneficial for dynamic, large-scale applications and offers improvements in speed and efficiency over existing methods.

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Amazon

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MultiXacts in PostgreSQL: usage, side effects, and monitoring

PostgreSQL uses a specialized structure called MultiXact IDs to manage concurrent access while maintaining data consistency
MultiXacts can introduce unexpected performance challenges or delays during vacuum operations, making it essential to monitor their usage
MultiXact IDs are a secondary data structure that tracks multiple transactions holding locks on the same row
The disk space usage for the pg_multixact directories triggers aggressive autovacuum operations to prevent MultiXact wraparound
You can use monitoring metrics like MultiXact ID age, storage size, and wait events to identify performance bottlenecks and manage MultiXacts more effectively
Strategies like consistent vacuuming, optimizing memory settings, and minimizing long-running transactions can mitigate performance issues caused by MultiXacts
By managing MultiXacts effectively, you can ensure smooth database operations under heavy transactional loads while preventing performance degradation and costly interruptions

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Cloudblog

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AlloyDB Omni supercharges performance: Faster transactions, analytics, and vector search

AlloyDB Omni version 15.7.0 brings significant improvements to PostgreSQL workflows.
Enhancements include faster performance, ultra-fast disk cache, enhanced columnar engine, and general availability of ScANN vector indexing.
AlloyDB Omni is more than 2x faster than standard PostgreSQL for transactional workloads.
The columnar engine speeds up analytical queries up to 100x compared to standard PostgreSQL.

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Dev

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EXPLAIN PLAN and DBMS_PROFILER

In Oracle SQL, EXPLAIN PLAN is used to understand the query execution path chosen by the optimizer, while DBMS_PROFILER is used for detailed performance profiling of PL/SQL code.
EXPLAIN PLAN shows the series of operations that Oracle will use to execute a SQL statement, including table scans, index scans, joins, and sorts.
DBMS_PROFILER captures execution counts, time spent, and identifies bottlenecks within PL/SQL functions, procedures, or packages.
EXPLAIN PLAN helps optimize SQL queries, while DBMS_PROFILER is best for refining PL/SQL code performance.

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Medium

410

Basic index strategy for SQL Server table

A clustered index defines the physical order of data in a table, while non-clustered indexes store a copy of the indexed column values along with a pointer to the actual data location.
Indexes offer benefits for SQL Server performance, including faster lookups and efficient searching on specific columns.
The choice of indexing strategy depends on factors like query patterns, data volume, and table structure.
Indexes require maintenance and optimization to ensure effectiveness and prevent performance degradation.

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Dev

262

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🚀 Go-ing Full-Stack: Building Dynamic Web Apps with Go 🐹, PostgreSQL 🐘, Docker 🐳, and HTTP Servers 🌐

This tutorial explains how to build a full-stack web application in Go using PostgreSQL, Docker, and HTTP servers. It covers setting up the project, connecting to PostgreSQL, setting up PostgreSQL with Docker, writing Go code to connect to PostgreSQL, and running and testing the app.
To get started, you need to have Go (version 1.15 or higher), Docker installed, and PostgreSQL installed either on your machine or Docker.
PostgreSQL is then run in a container using Docker to make it easier to manage and keep things isolated.
SQL queries are written to create a table to store user data
The Go backend for interacting with the PostgreSQL database is then written, with global variable DB that holds the connection pool for PostgreSQL.
The entry point of the application is handled by the main function that starts the server and defines the routes.
To test the app, you need to run the Go server and open the browser, where a list of users fetched from the database can be seen.
Building a full-stack app in Go is simple, and this tutorial provides a foundation for growing into more complex projects.
It also concludes with some humor advice for developers: "If it works, don’t touch it. Unless it’s Go – then Go for it!"

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Amazon

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Benchmark Amazon RDS for PostgreSQL with Dedicated Log Volumes

Amazon RDS is a managed service that provides a reliable and scalable PostgreSQL managed service to handle demanding workloads. PostgreSQL transactions are first logged in Write-Ahead Log (WAL) buffer before being logged and stored elsewhere.
Amazon now offers the Dedicated Log Volume (DLV) feature that enables customers to store WAL segments on separate storage. This ensures the performance of PostgreSQL transactions, as the storage system affects the performance of PostgreSQL transactions.
DLV enables write coalescing that reduces the overall Input/Output Operations Per Second (IOPS) demand for WAL through larger and more efficient I/O operations, increasing throughput and I/O efficiency, reducing high-performance storage's need and minimizing costs while ensuring optimal performance.
To benchmark the performance of Amazon RDS for PostgreSQL, you can use pgbench tool. This tool reveals insights on the Amazon RDS for PostgreSQL deployment's performance characteristics, enabling you to make a direct performance comparison between Amazon RDS with and without DLV feature enabled.
AWS CLI commands are required to launch Amazon RDS for PostgreSQL instances with and without DLV feature. The instances are created with a fixed size of 1,024 GiB and 3,000 Provisioned IOPS.
WAL statistics can be monitoring using AWS Cloudwatch and PostgreSQL’s pg_stat_wal, which provides detailed statistics about WAL activity. Performance benchmarks prove that DLV enhances PostgreSQL database write performance by reducing WAL contention effectively.
Benchmark results confirmed DLV’s effectiveness in mitigating WAL-related wait events, aligning with its intended function. DLV is compatible with PIOPS storage types (io1 and io2 Block Express) and works with PostgreSQL version 13.10 and higher, 14.7 and higher, and 15.2 and higher, across every region.
By using Amazon RDS for PostgreSQL with DLV enabled instead of without, processing time reduced by 92.5%, while transactions per second experienced an increase of 91.83%, and latency improved by 95.56%.
As an alternative to typical storage options, DLV offers enhanced write performance, significantly reducing WAL contention for write-intensive workloads that require fast-write operations and low-latency transactions.
To achieve optimal database performance, DLV offloads write operations to the DLV, freeing up the main database resources, increasing efficiency, and providing quicker data access, making it ideal for tasks demanding high performance consistently.

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Amazon

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New – Amazon DynamoDB lowers pricing for on-demand throughput and global tables

Amazon DynamoDB has announced price reductions for on-demand throughput and global tables on November 1, 2024, reducing on-demand throughput by 50% and global tables by up to 67%.
DynamoDB on-demand is now a more attractive option, providing a fully managed serverless experience that automatically scales in response to application traffic, with no capacity planning required.
DynamoDB effectively takes care of operation and scaling databases, reducing costs and simplifying management and provides seamless scalability as per traffic demands, ranging from hundreds of users to hundreds of millions of users.
Global tables pricing has been reduced by up to 67%, lowering replicated write pricing by 67% for on-demand tables and 33% for provisioned capacity tables.
DynamoDB on-demand couples with other AWS services such as Lambda, Amazon API Gateway and Amazon CloudWatch to provide a fully serverless application stack that handles variable workloads efficiently, without needing infrastructure management.
Global tables provide a fully managed, multi-active data replication solution without undifferentiated heavy lifting of replicating data across Regions.
There are several key benefits to choosing DynamoDB on-demand including No capacity planning, automatic scaling and usage-based pricing.
Global tables provide local performance and serve millions of requests per second without requiring capacity management, with 99.999% availability.
DynamoDB on-demand eliminates the need to monitor usage and make capacity adjustments, enabling customers to focus on building features for their customers.
DynamoDB in its latest price reduction makes on-demand the default and recommended mode for most DynamoDB workloads.

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VoltDB

275

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Top 5 AI Use Cases for IIoT: Enhancing Industrial Operations with Real-Time Data

As IIoT-based companies such as auto manufacturers leverage real-time data from connected devices, AI technologies are transforming how industrial organizations monitor, manage, and optimize their assets and use their data.
AI-driven preventative maintenance uses real-time data and machine learning (ML) algorithms to predict equipment failures before they happen.
AI algorithms, combined with IIoT data from visual sensors, thermal cameras, and sound detectors, can automate and enhance quality control processes.
Energy-intensive industries can use AI-driven energy management systems to recommend optimal energy usage patterns, automatically adjust HVAC systems and control lighting to minimize waste.
AI can optimize supply chains by analyzing data from sensors and GPS systems on vehicles, inventory systems, and demand forecasts.
By combining AI with wearable devices and IIoT sensors, organizations can monitor safety conditions in real time, detect potentially dangerous situations, and send alerts to prevent accidents.
AI and IIoT are a transformative combination, enabling industrial organizations to harness real-time data for smarter, faster decision-making.
Volt Active Data plays a crucial role in supporting these applications by providing a fast, reliable data platform optimized for real-time decision-making, ensuring that IIoT applications can meet the demands of modern industry.
For industrial companies looking to future-proof their operations, integrating AI and IIoT with a robust platform like Volt Active Data is essential for staying competitive.
From predictive maintenance to worker safety, these use cases highlight how AI-driven insights can improve industrial operations.

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Dev

155

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Connecting SQL Databases to the Cloud: PostgreSQL, MySQL, SQLite, and Cloud Integration Explained

SQL databases such as PostgreSQL, MySQL, and SQLite are popular among developers, but with the rising demand for cloud solutions, cloud integration has become essential.
Cloud computing offers a range of benefits such as scalability, high availability, and reduced infrastructure costs.
Cloud providers offer services such as Amazon RDS for PostgreSQL and MySQL, Google Cloud SQL for PostgreSQL and MySQL, and Azure Database for PostgreSQL and MySQL for easy and managed instance deployment.
Developers can connect to cloud-based PostgreSQL and MySQL instances using SQL management tools, while SQLite can be integrated with cloud storage solutions such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.
Ensuring the security of cloud databases is critical, and cloud providers offer built-in features such as IAM tools, firewalls, and 2FA.
By understanding how to connect and integrate SQL databases with cloud platforms, developers and IT teams can leverage the full potential of cloud databases to ensure performance, stability, and accessibility.

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