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Grading our 2024 enterprise technology predictions

The article evaluates the 2024 predictions made alongside Enterprise Technology Research's Erik Bradley and discusses whether they have held up to data so far. In terms of sustained budget pressures for global tech spending, midsize firms have landed within the 4-5% growth forecast, smaller firms appear to be facing greater budget pressures and the Global 2000 is tracking at 2.7% growth for the year. AI has not been a tide that lifts all ships, and those firms that are seeing ROI on AI projects are experiencing small 'wins' which are not self-funding. Visualization of code output is helping to lower the barrier for shipping generated code in real product use cases.
The prediction that cybersecurity would remain a top investment priority proved accurate, as did the focus on addressing sophisticated threats and regulatory compliance. Hybrid cloud models are gaining traction over aggressive public cloud migrations. Organizations across industries are prioritizing data literacy and addressing challenges such as AI hallucinations to ensure meaningful, actionable GenAI outputs. There has been a notable increase in demand for roles such as 'gen AI prompt engineers', ethical AI use and data literacy.
The GenAI trend is bifurcated, with 'me too' AI lagging behind leaders and hyper-scalers benefiting from the AI wave. Networking challenges in AI environments, particularly around latency and bandwidth, are becoming more prominent. Legacy players such as Dell, IBM, Oracle and HPE have leveraged AI and hybrid cloud offerings to remain competitive. Gen AI models are also creating a renewed focus on governance and data quality which benefits trusted ecosystems such as cloud.
Private-market shifts, M&As and IPOs are slowly picking up, but economic constraints haven't provided second lives for companies that are struggling to gain market share. Overall, the predictions balance well-established trends with forward-looking insights, keeping in mind the degree of difficulty. Looking into the year ahead, experts suggest that certain predictions such as hybrid cloud strategies and the rise of gen AI-driven skills and tools will continue to gain traction.

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Amazon

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Understanding how ACU minimum and maximum range impacts scaling in Amazon Aurora Serverless v2

Part 2 of the two-part blog post series explains how the minimum and maximum configuration of ACUs impact scaling behavior in Aurora Serverless v2 and the speed at which scaling occurs after it starts.
Aurora Serverless v2 provides an on-demand, auto scaling configuration for Amazon Aurora with the ACU as the unit of measure.
Each Aurora Serverless v2 workload requires unique minimum and maximum ACU requirements. Finding the right ACU configuration is essential.
Aurora Serverless v2 automatically scales the capacity of your database up and down in fine-grained increments called ACUs.
The scaling of Aurora Serverless v2 DB clusters works based on the workload on your database.
The scaling process of Aurora Serverless v2 is transparent and seamless and does not disrupt database operations or connections.
Observations revealed that Aurora Serverless v2 was responsive to scaling and had a more gradual scaling down process when higher ACU limits were set.
Based on the authors' findings, they recommend setting a balanced minimum ACU, setting a scalable maximum ACU, optimizing queries, and performing regular load testing to verify that the ACU settings can handle peak loads.
Aurora Serverless v2 offers a robust solution for businesses seeking flexible and cost-effective database management.
Authors of this article are Priyanka, Database Specialist Solutions Architect at AWS, and Venu Koneru, a Database Specialist Solutions Architect at Amazon Web Services (AWS).

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Amazon

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Understanding how certain database parameters impact scaling in Amazon Aurora Serverless v2

Amazon Aurora Serverless v2 is an on-demand, auto scaling configuration for Amazon Aurora.
When using Aurora Serverless v2, you specify a capacity range (minimum and maximum ACU values) for each database (DB) cluster.
Each ACU combines approximately 2 GiB of memory, CPU, and networking resources.
The post is Part 1 of a two-part blog post series and focuses on understanding how certain database parameters impact Aurora Serverless v2 scaling behavior for PostgreSQL-compatible DB instances.
To help you understand, there are test cases run through test cases on the following parameters that are most often tuned.
When using default parameters and no additional features enabled, the ACU scales down to 0.5 (minimum configured).
Using a nondefault configuration for max_locks_per_transaction impacts ACUs not scaling down below 2, and this might vary depending on the value you pick for this parameter.
Based on our findings, we recommend using a minimum ACU of 2 or higher if your DB cluster is supporting a high-connection workload.
In Part 2 of this series, we review how the minimum and maximum range of ACU influences scaling behavior of Aurora Serverless v2, tailored to different business scenarios.
The post authors are Venu Koneru and Priyanka. They both work at AWS as Database Specialist Solutions Architects.

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Insider

231

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Elon Musk is $119 billion richer this year — and the 10 biggest wealth gainers are up a combined $585 billion

The world's 10 biggest wealth gainers have grown a combined $585 billion richer in 2024.
Elon Musk, Larry Ellison, Jensen Huang, and Mark Zuckerberg have gained more than $70 billion each.
The buzz around artificial intelligence and market excitement following Donald Trump's reelection has boosted their companies' stock prices, benefiting them as the biggest shareholders.
Ten people have grown their personal fortunes by a combined $585 billion this year — a sum larger than the market value of Exxon Mobil ($535 billion), Oracle ($533 billion), or Mastercard ($478 billion).
Elon Musk is the CEO of automaker Tesla and spacecraft manufacturer SpaceX. He's also the owner of X, the social network previously known as Twitter, along with Neuralink, xAI, and The Boring Company.
Larry Ellison is the cofounder, executive chairman, and chief technology officer of Oracle, one of the largest enterprise software companies.
Jensen Huang is the founder and CEO of Nvidia, the graphics chip maker that has emerged as a critical seller of "picks and shovels" to the AI gold rush.
Mark Zuckerberg is the cofounder and CEO of Meta, the parent company of Facebook, Instagram, WhatsApp, and Threads.
Jeff Bezos is Amazon's founder, executive chairman, and former CEO.
Michael Dell is the founder and CEO of Dell Technologies, the maker of PCs, printers, and other computing equipment.

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Medium

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Why should table splitting be considered when a single table has 20 million records?

When a single table has 20 million records, table splitting should be considered.
The height of the B+ tree used by the InnoDB storage engine can impact query performance.
Controlling the height of the B+ tree within 3 to 4 layers is recommended for faster queries.
Estimating data volume based on the B+ tree structure and the size of data pages can help determine the need for table splitting.

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Medium

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Let’s Build a Shiny App from Scratch: A Mostly Useful Guide Using ‘shinymanager’

This article provides a guide on building a Shiny app from scratch using the 'shinymanager' package.
The article starts by mentioning the necessary requirements for developing the app, such as R, R Studio (or VS Code), and MySQL.
It then covers the file structure for organizing the project and explains the configuration file for MySQL password management.
The article goes on to discuss the UI file, including the inactivity function and variable assignments, and introduces the 'shinymanager' package for a secure app.

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Dev

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How to Install PostgreSQL on Ubuntu 22.04 LTS

PostgreSQL is an open-source RDBMS known for its advanced features and efficiency
This guide provides step-by-step instructions for installing and managing PostgreSQL on Ubuntu 22.04 LTS
NodeShift, a cloud provider, is used to launch a 2vCPU/4GB/10GB SSD virtual machine to host PostgreSQL
Steps for deploying a NodeShift compute node are provided, including selecting configuration options and an image
Before installing PostgreSQL, we need to install curl, retrieve PostgreSQL's repository key, and create source list for PostgreSQL's package
We need to configure postgresql.conf and pg_hba.conf before starting to use PostgreSQL
Creating a user, database, and tables in PostgreSQL are also covered with example queries
Using NodeShift with PostgreSQL ensures a reliable and scalable deployment environment
These steps will provide a robust and secure foundation for your database needs
PostgreSQL's flexibility and advanced features make it a popular choice for both developers and businesses managing large-scale data

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Infoq

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Microsoft Announces General Availability of Fabric API for GraphQL

Microsoft has launched Fabric API for GraphQL, moving the data access layer from public preview to general availability (GA).
This release introduces several enhancements, including support for Azure SQL and Fabric SQL databases, saved credential authentication, detailed monitoring tools, and integration with CI/CD workflows.
The release also includes a monitoring dashboard that provides a clear view of API activity, helping developers analyze usage patterns, troubleshoot issues, and optimize performance.
The Fabric API for GraphQL now supports integration with GitHub and Azure DevOps repositories, allowing developers to maintain version control for API definitions.

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Dev

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A Comparison of Top Private and Browser Based SQL on CSV Tools

Several online tools cater to the need of running SQL queries on CSV files.
In this comparison, we analyze CSV SQL Live, CSV Fiddle, Dirty Little SQL, and CSV SQL Tool.
CSV SQL Tool stands out as the ultimate choice due to its modern UI, schema display, and result downloads.
Compared to competitors, CSV SQL Tool offers a well-rounded experience for professionals and casual users.

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Dev

156

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How to Count Rows in All MySQL Tables Using a Bash Script

To count rows in all MySQL tables using a Bash script, you can create a script that queries each table and returns the row count for each table.
The script requires MySQL login credentials, retrieves all tables in the database, and loops through each table to count the rows.
To execute the script, make it executable and run it on the Unix-like system with Bash available.
The output shows the table name followed by the corresponding row count.

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Medium

272

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Query Files from AWS S3, Azure, and GCS as Tables, and Run Data Science Algorithms in Just 5…

UnifyML is a downloadable software that allows you to query files from AWS S3, Azure, and GCS as regular SQL tables.
It enables you to run machine learning and data science algorithms on these tables without the need for complex data pipelines.
UnifyML is compatible with various environments including on-premise setups, cloud, Docker, and Kubernetes.
The software can be easily installed and used with SQL client tools like DBWeaver, Zeppelin, and JDBC drivers.

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Dev

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Beyond LIKE: Advanced Text Search and Keyword Matching in Postgres using Full Text Search

Postgres provides built-in full-text search functions that address the limitations of basic operators like LIKE. These limitations include substring matching, ranking, synonym recognition, phrase-based matching and computationally expensive scans as data grows. Postgres’s functionality involves using the to_tsvector() function that tokenizes the video title into searchable tokens used for comparisons. Lemmatization then makes these searchable tokens lower-cased, removes suffixes and stop words by using dictionaries. The tokens are then stored in a column of type tsvector for faster searches. Furthermore, a GIN index rather than a regular B-tree index can be created on column for speedier searches. Coalesce(title, '') can be used to handle null values and matches can be found using the to_tsquery() function that converts a query string into tokens to match. PostgreSQL offers many advanced features for full-text search in the documentation.
Basic operators like LIKE are limited in terms of substring matching, ranking, synonym recognition and phrase-based matching. Full-text search functions offer a better solution.
Postgres provides built-in full-text search functions that address the limitations of basic operators like LIKE and computationally expensive scans as data grows.
The basic process for full-text search in Postgres involves using the to_tsvector() function that tokenizes the video title into searchable tokens that can be compared.
Lemmatization makes the tokens lower-cased, removes suffixes and stop words, and standardizes the data using dictionaries.
This transformed data is then stored in a column of type tsvector for faster searches.
A GIN index can be created on the column to speed up the text search process even more. Coalesce(title, '') can be used to handle null values.
The to_tsquery() function converts a query string into tokens to match. Matches can be found using @@, the match symbol for full-text search.
PostgreSQL offers many advanced features for full-text search, which users can explore in the documentation.
In summary, a full-text search provides a more efficient way of finding matches compared to basic operators. Postgres has built-in full-text search functions that address the limitations of basic operators.

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Medium

437

Stored Procedures vs Functions: A Comprehensive Guide to SQL Essentials

A stored procedure retrieves employee details and returns an output parameter, while a function only returns the employee name as a result.
Functions are used in SELECT queries to calculate derived columns, whereas stored procedures cannot perform such calculations.
Stored procedures can handle error conditions like 'divide-by-zero' by using exception handling, whereas functions assume valid inputs.
Joining a function that calculates discounts with a product table is more efficient than executing a procedure for each discount calculation.

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Siliconangle

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Three insights you might have missed from KubeCon + CloudNativeCon NA

Artificial intelligence is driving innovation in cloud-native technologies, streamlining processes and enhancing decision-making across industries.
Automation and innovation remains central to unlocking Kubernetes' full potential, according to Mike Barrett, vice president and general manager of hybrid cloud platforms at Red Hat Inc.
Kubernetes AI advancements play a pivotal role in enhancing scalability and flexibility for AI workloads, with Nutanix Inc and MinIO Inc offering scalable and secure AI infrastructure.
Open-source ecosystems continue to be the foundation of innovation in Kubernetes AI, enabling organizations to scale solutions while fostering collaboration across industries.
Enterprises are embracing advanced tools to transform legacy systems and drive operational efficiency, with Red Hat's enhanced OpenShift Virtualization platform providing hybrid cloud solutions.
Kubernetes AI models are being used to efficiently queue tasks across multiple cloud environments, as modern observability tools identify inefficiencies and help identify significant cost savings for enterprises.
Recent upgrades to Google Kubernetes Engine have expanded its capacity to support clusters of up to 65,000 nodes, meeting the scalability needs of modern AI applications.
Intel Corp's Open Platform for Enterprise AI provides a vendor-neutral framework that integrates more than 30 cloud-native microservices, allowing customized AI applications with single-click deployment.
Oracle Corp's contributions to the Cloud Native Computing Foundation further emphasize the critical role of Kubernetes AI in advancing generative models and simplifying service launches, prioritizing cross-sector collaboration and innovation.
Red Hat is driving Kubernetes AI innovation through its collaborative Kubernetes solutions to create accessible, cost-effective tools for domain-specific applications and addressing the challenges of deploying, scaling AI workloads.

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Javacodegeeks

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java.lang.classnotfoundexception: com.mysql.cj.jdbc.driver Resolved

The error java.lang.classnotfoundexception: com.mysql.cj.jdbc.driver occurs when your Java application cannot locate the MySQL JDBC driver class at runtime.
The ClassNotFoundException arises when the JVM cannot find the specified class in the classpath. Common reasons include missing MySQL JDBC Driver dependency, incorrect classpath configuration, or using an outdated/incompatible version of the driver.
To resolve the issue, add the MySQL JDBC Driver Dependency to your project by including the appropriate dependency in your build file (e.g., pom.xml for Maven or build.gradle for Gradle). After adding the dependency and ensuring proper classpath configuration, the error should be resolved.
The java.lang.ClassNotFoundException com.mysql.cj.jdbc.Driver error can be fixed by adding the required MySQL JDBC dependency and ensuring proper classpath configuration.

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