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HadiDB: A Lightweight, Horizontally Scalable Database in Python

HadiDB is a lightweight, highly horizontally scalable database written in Python.
The installation of HadiDB can be done using pip install command.
The code examples show how to create users, databases, collections, and schemas using HadiDB.
Additional operations such as inserting data, updating data, retrieving data, counting documents, deleting data, and managing databases and collections are also demonstrated.

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Amazon

19h

133

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Comparison of test_decoding and pglogical plugins in Amazon Aurora PostgreSQL for data migration using AWS DMS

AWS DMS is a popular service for homogeneous and heterogeneous database migrations and uses logical replication slots to reduce downtime during migration.
AWS DMS supports test_decoding and pglogical plugins for logical decoding for PostgreSQL users.
Logical_decoding_work_mem is a key parameter that impacts the CDC operations and hence efficiency of data migration.
Tuning logical decoding memory can reduce disk spills and enhance resilience and performance.
pglogical plugin offers filtering of non-CDC tables at the replication slot and reduces network bandwidth needed.
Choosing the right logical decoding plugin and adhering to migration best practices can maximize the efficiency of data migration.
Logical_decoding_work_mem can be adjusted to improve AWS DMS CDC performance.
Optimizing and adjusting the logical_decoding_work_mem parameter can reduce spills to disk.
Choosing the correct database instance size, monitoring with alerts and migration tasks is essential.
The pglogical plugin is suitable for workloads that do not involve all tables being part of the CDC process.

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Amazon

19h

366

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Enhance the reliability of airlines’ mission-critical baggage handling using Amazon DynamoDB

Problems such as departure scanning delays, loading irregularities, and manual flight closing lead to mishandling, misplaced, or damaged baggage resulting in poor customer experiences. Airlines’ traditional systems often rely on manual processes and outdated systems that pose challenges in quickly adapting to the dynamic nature of airline business processes.
The solution proposed by IBM Consulting involves decomposing monolithic database systems into sub-capabilities that align with domain-driven design principles. Amazon Web Services (AWS) solutions like Amazon DynamoDB global tables are used for the solution which aims to achieve 99.999% availability by using separate read and write capacities across separated capabilities with each having its table.
The proposed solution uses Amazon DynamoDB global tables to meet the low latency requirements, with near-zero recovery point objective (RPO) and Recovery Time Objective (RTO) while optimizing costs to handle traffic bursts during irregular operations. It also facilitates the integration of IoT devices, including RFID scanners.
Each capability has a corresponding microservice and separate Amazon DynamoDB global tables ensuring baggage check-in systems, reservations, and loading systems use tables and microservices based on data ownership.
DynamoDB lacks built-in aggregation, but you can use precomputed aggregates instead that optimize queries, improve performance, and reduce latency by accessing precomputed data.
A data migration strategy is required to support coexistence during the phased rollout of a baggage handling system across various stations. AWS Database Migration Service (AWS DMS) is used for continual migration of data from on-premises relational databases to DynamoDB.
This solution using Amazon DynamoDB offers numerous benefits, including elastic scalability, flexibility in response to changing requirements, providing agility, and adapting to dynamic trends. It offers built-in fault tolerance, providing uninterrupted service even in the face of unforeseen challenges in the airline industry.
IBM Consulting is an AWS Premier Tier Services Partner that helps customers use AWS to drive their business transformation. For additional information, please contact an IBM Representative.

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Cloudblog

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Google is a Leader, positioned furthest in vision in the 2024 Gartner Magic Quadrant for Cloud Database Management Systems

Google has been named as a Leader in the 2024 Gartner Magic Quadrant for Cloud Database Management Systems for the fifth year in a row.
The search giant is positioned furthest in vision among all vendors evaluated; it is a testament to Google’s innovations, helping tens of thousands of Data Cloud customers across industries to build applications faster and unlock deeper insights through a unified data and AI approach.
61% of organizations are evolving their data and analytics operating model due to AI technologies.
Google provides a unified, intelligent, open Data Cloud, built on planet-scale infrastructure with AI at its core to address the most pressing data challenges facing customers today.
Google's Data Cloud is committed to being the most open platform, offering unmatched flexibility to meet diverse customer needs.
Google offers end-to-end unified data platforms, accelerates innovation with AI-assistive and agentic experiences and open data ecosystems for the gen AI era.
Google has recently introduced BigQuery Unified Platform and multi-model data support like Spanner Graph, promoting intelligent capabilities that enable customers to deliver a new class of AI-enabled applications.
AI-assistive experiences to deepen the integration of AI capabilities with Data Cloud helps businesses to unleash new possibilities.
Google cloud's open data ecosystem partners with technology providers to deliver choices and solutions to customers across a robust partner ecosystem.
Google has seen remarkable growth with customers in the past five consecutive years by leveraging cutting-edge technology to customers faster.

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Dev

266

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SQL Filtering and Sorting with Real-life Examples

This blog explains the SQL clauses like WHERE, HAVING, ORDER BY, GROUP BY, and other related clauses using real-life examples with the employees and departments tables.
The WHERE clause lets you filter records based on specified conditions.
The GROUP BY clause is used to group rows with same values into summary rows.
The HAVING clause is used to filter groups created by GROUP BY clause after the aggregation.
The ORDER BY clause is used to sort the result set by one or more columns.
The LIMIT clause is used to specify the number of records to return from the result set.
The DISTINCT clause is used to return only distinct values, removing duplicates from the result set.
The AND, OR, and NOT operators are used to combine multiple conditions in the WHERE clause.
The AND operator combines the conditions where all conditions must be true.
The OR operator is used when only one of the conditions must be true.

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Insider

103.9k

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4 Big Tech product managers and an engineer share negotiation tips that nabbed them thousands of dollars in better comp

Tech professionals from Meta, Google, Cisco, and Oracle share salary negotiation strategies that helped them secure significant pay raises.
Sarra Bounouh, a Meta product manager, avoids discussing salary early and prefers negotiating after receiving an offer.
Ketaki Vaidya, who moved from Oracle India to California, used her internal credibility to secure a higher salary, bonuses, and relocation perks.
Varun Kulkarni of Cisco negotiated a 5% increase by transparently discussing competing offers.
Hemant Pandey, a senior software engineer at Meta, used offers from other companies to boost his pay and negotiated a sign-on bonus and stock options.

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Dev

232

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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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Sanjeevvellaisamydba

282

How to Start and Stop Oracle Data Guard Server 19c

To stop the Oracle Data Guard server, first, stop the listener service using 'lsnrctl stop '.
Next, cancel the managed recovery process by connecting to the database and executing 'ALTER DATABASE RECOVER MANAGED STANDBY DATABASE CANCEL;'
Finally, shut down the DR database using the command 'SQL> SHUT IMMEDIATE;'
To start the Oracle Data Guard server, start the listener service using 'lsnrctl start '.
Then, start the database in NOMOUNT mode using 'SQL> STARTUP NOMOUNT;'.
After that, mount the standby database using 'SQL> ALTER DATABASE MOUNT STANDBY DATABASE;'.
Lastly, initiate the managed recovery process by executing 'SQL> ALTER DATABASE RECOVER MANAGED STANDBY DATABASE DISCONNECT FROM SESSION;'

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Javacodegeeks

280

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Securely Connect to a Remote MySQL Database Over SSH in Java

The article explores how to establish a Java SSH remote MySQL DB connection, enabling Java applications to securely connect to a remote MySQL database by tunneling through an SSH server.
SSH tunneling is necessary because it provides a secure, encrypted channel between the client and the remote server, ensuring that sensitive data is not exposed over insecure networks.
Dependencies required for SSH tunneling and MySQL database connectivity are included in Java, including the jsch library to establish the SSH connection and the mysql-connector-java library to enable Java applications to connect to a MySQL database.
A sample code is also provided to understand how to establish a Java remote MySQL DB connection using SSH tunneling.
Class-level configurations include SSH details for server authentication and MySQL settings for database connectivity. The Session object handles the SSH connection lifecycle and enables port forwarding.
The process of establishing an SSH connection with the JSch library and forwarding traffic securely to the MySQL server is explained.
By setting up proper configurations for both SSH and MySQL, secure and efficient communication channel between the Java application and the remote database is ensured.
The jsch library is used to establish the SSH connection and manage port forwarding, while the mysql-connector-java library enables Java applications to connect to a MySQL database.
SSH tunneling establishes a secure connection to a remote server by creating an encrypted channel through which data is transmitted. It enables the forwarding of local traffic securely to a remote database server.
Therefore, the Java SSH remote MySQL DB connection helps to ensure the secure and efficient communication between Java applications and remote databases.

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Inside

17h

272

JavaOne: A Lifetime of Java Learning in Three Days

JavaOne 2025 offers a range of learning tracks for Java enthusiasts.
Developers can advance their Java expertise and explore areas like application performance, artificial intelligence, and cloud productivity.
The official launch of Java 24 will take place at JavaOne 2025, with insights into new features.
JavaOne 2025 provides opportunities for networking, community building, and enjoying the journey with fellow Java enthusiasts.

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Amazon

19h

229

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Enhancing performance of Amazon RDS for Oracle with NVMe SSD hosted Smart Flash Cache and Temporary Tablespaces

Amazon RDS for Oracle now supports Non-Volatile Memory Express (NVMe) SSD devices in physical attachment and optimizing low latency, random I/O performance, and sequential read throughput for temporary tablespaces and flash cache features.
NVMe SSD instance store helps provide higher throughput for latency-sensitive workloads, providing an alternative to using large RDS for Oracle instances where the workload's resource demand is higher.
Flash cache is useful for single-block random reads, and it is recommended to size the cache to hold most of an active dataset to reduce input/output costs and increase response time.
Benefits of placing temporary tablespaces in instance storage include reduced read latency, higher throughput, and less need to provision Input/output operations per second (IOPS). It also frees up space from the RDS instance storage, and temporary files that are not backed up will not be included in RDS instance backups and snapshots.
You can customize the allocation of the temporary tablespace and the flash cache on the instance store through the RDS parameter group using values from DBInstanceStore with 0-10 in increments of 2.
Flash cache can be used with Amazon RDS Performance Insights to list DB load sliced by waits and observe top wait events like DB file sequential reads, looking at IOPS and average queue length in Performance Insights and the read latency on Amazon CloudWatch metric.
Oracle's algorithms partition each buffer into hot or cold areas, with each accessed block moving to the head of the data block chain on the hot side. The instance store can be used to create an additional flash cache that stores hot buffers to improve read input/output and increase response time.
NVMe SSD local storage for temporary tablespaces showed considerable performance gains in reducing the wait time, with a 61% reduction in SQL execution time for a create table statement that used sorts, joins, and aggregations.
NVMe SSD local storage for flash cache showed a 94.79% improvement in query performance in TPC-C-like benchmark workloads, cutting down read input/output, and replacing wait events.
By leveraging RDS for Oracle instances with NVMe SSD storage, you can provide a cost-effective way of facilitating high-throughput in latency-sensitive workloads.

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HRKatha

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Sekhmet Pharmaventures welcomes Nivedita Agrawal as senior director-HR

Sekhmet Pharmaventures has appointed Nivedita Agrawal as its senior director-human resources.
Agrawal brings two decades of experience and was previously the general manager-HR at Freyr Energy Services.
At Sekhmet, Agrawal will focus on developing and implementing talent strategies, cultivating high-performing teams, and fostering leadership.
Her appointment reflects Sekhmet Pharmaventures' commitment to growth and innovation.

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2 Likes

Medium

259

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Teradata Fastload alternatives in MySQL and PostgreSQL.

Fastload is a Teradata SQL feature for high-speed bulk loading, used for data acquisition and application phases.
In MySQL, there is no direct alternative to Fastload, but loading from a CSV file, using MySQL Shell, or multi-row inserts can be utilized.
For PostgreSQL, the COPY command, pg_bulkload extension, and parallel import with psql are recommended methods for efficient data loading.
These alternatives can enhance query speed and performance in their respective SQL dialects.

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Medium

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Generating SQL queries from natural language

This post discusses the process of generating SQL queries from natural language using a sample database called AdventureWorks2014.
The database schema is loaded to provide structure and instructions to the language model (LLM).
The LLM is prompted with a task to generate an SQL query based on the provided database schema and a natural language question about the database.
The generated SQL query is obtained, cleaned, and executed to retrieve the desired information.

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4 Likes

Dev

184

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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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