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Documentum – Impact of Java 17 and JAVA_TOOL_OPTIONS

Starting with JDK 9, you may need to add some “–add-exports” or “–add-opens” parameters to the JAVA_TOOL_OPTIONS. With Java 17, it is fully in effect so you may start seeing some issues if you don’t have the correct configuration.
Documentum 23.2 and 23.4 added full support for JDK 17, but not defining JAVA_TOOL_OPTIONS when trying to install and run Documentum might result in some issues.
Installing the Documentum binaries without defining the JAVA_TOOL_OPTIONS doesn’t generate any errors or warnings, but it might cause a silent failure with the DFC installation.
Without JAVA_TOOL_OPTIONS, the DFC doesn’t get installed with the Documentum binaries but the rest looks good. Manually installing DFC would work.
Installing a Connection Broker without defining the JAVA_TOOL_OPTIONS doesn’t cause any issues, but connecting to it will require a DFC.
Trying to run the Repository installation without defining JAVA_TOOL_OPTIONS will cause the installation to quickly fail because of its dependency on DFC.
Defining JAVA_TOOL_OPTIONS is required for both the Documentum Server binaries and the Repository installation. It also helps at runtime with utilities such as dmqdocbroker/iapi/idql.
OpenText has included Java options in several places, but it's still not enough for a fully functional setup.
Overall, adding the correct Java options will help ensure a smooth installation and usage of Documentum.

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Amazon

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Amazon DynamoDB data models for generative AI chatbots

Generative artificial intelligence (AI) chatbots continuously learn from their interactions to provide real-time, context-aware responses, making them effective for customer service and personal assistants.
Amazon DynamoDB is an ideal storage solution to store chat history and metadata due to its scalability and low latency.
Access pattern definitions are crucial in data modeling for DynamoDB to achieve a flexible and scalable database schema that optimizes latency and throughput of specific queries.
When creating an optimal data model for chatbots, it is helpful to break the data into smaller chunks using vertical partitioning and group related information under a single partition key to create an item collection.
Amazon DynamoDB Time-to-Live (TTL) feature makes sure chat and message items are automatically deleted after a certain number of days, simplifying storage management and avoiding additional deletion costs.
Python and Boto3 are excellent resources to implement data access patterns for Amazon DynamoDB and use case scenarios like ListConversations, GetChatMessages, CreateChat, PutMessage, EditMessage, and DeleteChat.
By implementing effective data modeling strategies like vertical partitioning and making use of AWS resources like Amazon DynamoDB, generative AI chatbots can offer a seamless and scalable user experience that optimizes performance, enhances personalization, and drives customer satisfaction.
Lee Hannigan, a Sr. DynamoDB Specialist Solutions Architect, suggests that to take the generative AI chatbot to the next level, one can benefit from the comprehensive documentation and full capabilities of NoSQL Workbench for DynamoDB to create and optimize a chatbot's data model.

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Amazon

268

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Build a scalable, context-aware chatbot with Amazon DynamoDB, Amazon Bedrock, and LangChain

Developers can create chatbots that scale seamlessly while maintaining conversation history across multiple sessions with Amazon DynamoDB, Amazon Bedrock, and LangChain.
Context awareness is important for building conversational AI that feels natural, engaging, and intelligent. In a context-aware chatbot, the bot remembers previous interactions and uses that information to inform its responses, much like a human would.
Using DynamoDB with LangChain to manage chat history offers several advantages such as enhanced user experience, seamless integration, and enhanced scalability. The combination allows chatbots to deliver a consistent and personalized experience for users, retrieve chat messages with minimal overhead, and manage conversation history efficiently.
LangChain is a framework designed to simplify the creation and management of advanced language model applications. It provides tools to integrate various back-end systems like DynamoDB for storing chat history and Bedrock for generating intelligent responses.
The DynamoDB integration is centered around the DynamoDBChatMessageHistory class, which abstracts the complexities of storing and retrieving chat history from DynamoDB.
To maintain context across interactions with chatbots, developers can use LangChain’s RunnableWithMessageHistory class, which makes sure that each interaction with the chatbot is informed by the full conversation history stored in DynamoDB.
Developers can also use Streamlit to build and deploy web applications for testing AI applications with a few lines of Python code.
DynamoDB and Amazon Bedrock offer a scalable solution for building chatbots that can remember and use conversation history effectively.
With all the components put together, developers can create context-aware chatbots that deliver natural and engaging user experiences.
Overall, the approach described in this article enables developers to build robust and intelligent chatbots with ease, providing users with responsive, relevant, and personalized conversational AI experiences.

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Crypto-News-Flash

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XRP Ledger Activates Crucial Price Oracle Amendment: Here’s Why It Matters

The XRP Ledger has activated the Price Oracle amendment, allowing integration of off-chain data through oracles.
This update enhances the DeFi capabilities of the XRPL and expands its decentralized applications.
With Ripple's plan to introduce smart contract support, the amendment positions XRPL competitively against other blockchain platforms.
There is positive market sentiment and speculation that this update may drive up demand for XRP.

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Infoq

131

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Expedia Migrates a Massive Cassandra Cluster to ScyllaDB with Zero Downtime

Expedia Group migrates a massive Cassandra cluster to ScyllaDB with zero downtime
The migration was motivated by ScyllaDB's built-in Change Data Capture (CDC) capabilities
The migration process included transferring 1 TB of data, ensuring zero downtime
The migration to ScyllaDB improved efficiency, stability, and cost-effectiveness for Expedia

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Dev

154

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Top 8 MySQL Schema Checks to Boost Database Performance

Defining Primary Keys help in optimizing query results. Lack of primary keys slows down replication performance, especially with row-based or mixed replication. The preferred Table Engine for most cases is InnoDB due to its superior performance, data recovery capabilities, and transaction support. InnoDB caches both data and indexes in memory, which is preferred for read-heavy workloads. Using different collations across tables or even within a table can lead to performance problems, particularly during string comparisons and joins. Mixed character sets can hurt join performance on string columns by preventing index use or requiring value conversions. Tables that are expected to grow indefinitely and use auto-increment for primary keys should use the UNSIGNED BIGINT data type. Foreign keys can impact database performance because each write operation requires additional lookups to verify the integrity of the related data. Remove Unused or Duplicated Indexes to streamline query optimization and reduce overhead. Unused indexes can negatively impact database performance by consuming disk space, increasing processing overhead and slowing down operations.
Primary Key Check (Missing Primary Keys)
Table Engine Check(Deprecated Table Engine)
Table Collation Check (Mixed Collations)
Table Character Set Check (Mixed Character Set)
Column Auto Increment Check(Type of Auto Increment Columns)
Table Foreign Key Check(Existence of foreign keys)
Duplicated Index Check
Unused Index Check
Releem now includes comprehensive schema health checks. These checks provide real-time insights into your database’s structural integrity, along with actionable recommendations for fixing any detected issues.

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Hackernoon

364

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ChartDB: Pioneering the Future of Database Visualization ✨

ChartDB is a database visualization tool that has gained popularity on GitHub, with over 1,500 stars.
It offers an intuitive interface for easy database visualization and management.
Future plans for ChartDB include AI-driven features, community-powered growth, and expanding functionality.
The founders are focused on innovation and setting a new standard in database visualization.

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Dev

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Something Would Free Data Scientists from Heavy Coding Work

SQL code becomes complicated in complex situations which makes it hard to debug and optimize.
Writing complicated SQL statements is a very inconvenient process.
Introducing esProc SPL as a tool for structured data processing which is both simple and versatile.
SPL is a proprietary file format that is portable.
SPL supports big data processing, in-memory computation and can perform parallel processing.
Python has many limitations, Python struggles with datasets larger than the available memory.
SPL supports a wide range of high-performance algorithms and thus can achieve a dramatic skyrocketing on computational performance.
Python does not have any big data processing capabilities and is time-consuming in processing big data.
SPL comprehensively offers debugging capabilities including Set breakpoint, Run to cursor, Step over, etc.
Both SQL and Python are not satisfactory enough for data processing.

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Medium

2.5k

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Exploring PostgreSQL: The Advanced Open-Source Relational Database

PostgreSQL, also known as Postgres, is an advanced open-source relational database system.
Key features of PostgreSQL include SQL compliance, ACID compliance, and extensibility.
It also offers robust support for JSON, concurrency and performance, full-text search, and replication.
PostgreSQL is widely used in web applications, geospatial applications, data warehousing, financial systems, and scientific research.

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HRM Asia

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CHROs, here are five opportunities to lead the future of work

New data indicates that more than 90% of CEOs say HR should have a hand in developing an organisation’s future of work strategy.
However, in most cases, the people team is not leading the organisation’s future of work initiatives.
A September 2024 IBM Institute for Business Value and Oracle report identified a group of forward-thinking organisations that are already succeeding with their strategic approach.
IBM and Oracle found that what sets these organisations apart is their comprehensive planning approach.
These “visionary” organisations report immediate ROI from their future of work strategies.
HR leaders have a crucial role in shaping their organisation’s future of work strategy.
IBM and Oracle researchers predict that success will depend on HR’s ability to champion change, develop talent and create environments where both people and technology thrive.
Here are areas where CHROs and their teams can have an impact: Talent development and management, employee experience enhancement, technology integration, change management, and skill development.
Creating environments that support continuous learning and leveraging AI to provide personalised development opportunities are key.
The study highlighted the importance of recognising employees with natural potential.

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Dev

282

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How indexes power your database

Indexes are used in databases to retrieve information as fast as possible by providing an additional data structure that provides references to data in disk
The typical implementation of indexes use variations of the B-Tree data structure to store references to the pages
The binary search has logarithmic time complexity and is generally faster than linear complexity
Creating an index for a table means creating a separate table that includes mapped column data
The database would scan this index table rather than every row of the original table when a user tries to search for a specific column's value
In this article, we explored how indexes can power our databases and speed-up reading operations
We saw that indexes uses binary search instead of sequential access in full table scan, which increases its efficiency
Through the practical example, we understood how the database plans the query execution on tables with and without indexes
It is important to understand how to optimize our queries to retrieve information as fast as possible
This is crucial for databases with large datasets to avoid the traversal millions of rows when searching for a single user, etc

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Siliconangle

351

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Why Jamie Dimon is Sam Altman’s biggest competitor

Investors have poured north of $30 billion into independent foundation model players focusing on the false grail of so-called artificial general intelligence (AGI). However, we believe the greatest value capture exists in what is called Enterprise AGI. Private enterprises will capture the majority of value in the race for AI leadership. This report will dig deep into the economics of foundation models and will analyze Enterprise AGI and the untapped opportunities that exist within enterprises.
The false grail of AGI is often associated with OpenAI Chief Executive Sam Altman, driven by the pursuit of machine business work better than humans. However, removing humans' involvement for decision-making is not as easy as it seems.
AGI in the enterprise is the ability gradually to learn and adapt white-collar work processes of the firm. Instead of one all-intelligent AGI, it is a swarm of modestly intelligent agents that can collectively augment human white-collar work. Nvidia CEO’s quotes also resonate with this idea of a directory of AIs, some digital, some biological, specialized and skilled, and just generally good at doing things. An all-knowing AGI may not be a viable scenario.
Private enterprises possess unique data and process advantage that is not in the public domain, which is the key ingredient for its competitive advantage. The proprietary knowledge specific to a business resides in the enterprise's data estate, with foundation models deriving from the transformer, differentiated solely by the data sets that train it.
LLMs are focused on scaling up its algorithm without recognizing that scaling up Models does not mean scaling their accuracy. The cost of scaling models is becoming unsustainable, and prices will drop like a rock. The investments going to foundation models are misguided, and the massive value capture exists in Enterprise AGI, which is a large opportunity for companies and most of the value created will accrue to these firms.
Citizen developers give agents goals and guardrails. But just as important, agents can generate plans with step-by-step reasoning that humans can then edit and iterate. Then the exception conditions become learnable moments to help get the agent further down the long tail the next time. Agents can learn from their human supervisors and learn from exceptions while in production. This is the opposite of traditional software where you want to catch and suppress bugs before production. This extension of what an agent can do can create a potentially learnable step to extend the long tail of activities and handle more edge cases.
Agents can change the economics of achieving that by observing and learning from human actions. The swarm of workflow agents, which are really specialized action models, collectively have the ability to learn and embody all that management knowhow and outperform the most advanced foundation model. The collective intelligence of those agents outperforms the singular intelligence in a frontier foundation model.
The enterprise automation opportunity is significant but requires specialized knowledge because each process is unique, and it takes more than an AI model to understand an enterprise's processes. The future of enterprise software must not only deliver transactional efficiency and productivity but also be capable of managing and analyzing the complexities of real-time business processes in a unified framework subject to operational, analytic and historical systems.
Though there are various emerging players exploring the integrated, process-driven source of truth, understanding the nuances of each approach will be critical to building applications that can capitalize on this integrated, process-driven source of truth. The future presents both challenges and a significant opportunity, setting the stage for a new era in enterprise applications, data orchestration, and process alignment.
Overall, we believe that Enterprise AGI provides a massive opportunity for companies, and most of the value created will accrue to these firms. Private enterprises will capture the majority of value in the race for AI leadership, underscoring the importance of Enterprises' ability to gradually learn and adapt white-collar work processes over an all-knowing AGI.

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Medium

145

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Understanding SQL Foreign Keys and Joins for Effective Data Management

A foreign key is a field (or collection of fields) in one table that uniquely identifies a row of another table.
The purpose of a foreign key is to maintain referential integrity between the two tables.
In this article, we explored the concept of foreign key relationships in SQL, demonstrated how to create tables and insert data, and performed a JOIN operation to retrieve combined data from related tables.
Understanding these fundamentals is essential for designing efficient databases and performing complex queries to gain insights from your data.

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Medium

159

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Creating a PHP MySQL login page

This article provides an in-depth guide on how to create a functional and secure PHP MySQL login page.
Introduction to PHP MySQL Login Systems.
Setting Up the Development Environment, including installing XAMPP and creating a new project folder.
Database design for user authentication, including creating the database and table and setting up database credentials in PHP.

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Dev

296

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Mastering MySQL Views

MySQL views are virtual tables that simplify queries, enforce security, and encapsulate business logic.
Views enhance data safety by restricting user access to sensitive data without disrupting the underlying tables.
MySQL views can improve the performance of queries by minimizing the data retrieval and query complexity.
Before creating views, identify use cases such as data aggregation, reporting, and user-specific data access.
Analyze the existing schema, including table structures, relationships, and indexes, before crafting queries.
Optimize views' performance through data retrieval minimization, early data filtering, indexing, and avoidance of complex views.
MySQL does not natively support materialized views, but they can be emulated using tables that contain aggregated results.
Good documentation and regular updates are necessary for views' maintenance.
MySQL views can be used effectively for data management by following best practices to create and maintain them.
MySQL views can be a valuable asset in improving data accessibility and productivity of businesses.

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