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A High-Level Overview of Reflector Oracle Protocol

Reflector Oracle Protocol is a decentralized blockchain oracle built on the Stellar Network.
It distinguishes itself with features like curated node network, multisig-protected consensus, user-friendly interface, and advanced data helpers.
Reflector ensures trust, security, accessibility, and timely updates for various use cases including DeFi, insurance automation, prediction markets, gaming platforms, and supply chain management.
With its focus on real-world applications and leveraging Stellar's scalability, Reflector is poised to drive innovation in the blockchain oracle space.

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Dev

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SQL in CouchDB: Get SQS for FREE and Celebrate Our Birthday

Structured Query Server (SQS) is a solution to bring the power of SQL to CouchDB.
Neighbourhoodie is celebrating its 10th birthday by offering 5 free SQS licenses to the CouchDB community.
SQS aims to bridge the gap between relational and document-based databases without burdening users with complexity.
SQS allows users to query and retrieve data from a CouchDB database using SQL SELECT statements.

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RandomThoughts

Postgres and Migrating Databases

It is possible to transfer data from an Oracle database structure to a similar structure in Postgres.
Foreign Keys can pose a challenge during data transfer, as they are automatically checked in Postgres.
To temporarily disable Foreign Key checks in Postgres, triggers can be disabled and re-enabled.
After data transfer, it is important to check if referential integrity is maintained in the Postgres database.

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Amazon

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Join your Amazon RDS for Db2 instances across accounts to a single shared domain

Amazon RDS for Db2 is a fully managed solution that offers high performance within minutes, featuring an optional Multi-AZ deployment that synchronously replicates data to a cold standby DB instance providing high availability and reliability.
You can create an Amazon RDS for Db2 instance by using methods such as the AWS Management Console, AWS CLI, AWS CloudFormation, Terraform by Hashicorp.
In this post, AWS Managed Microsoft AD from an AWS account is used to provide Microsoft AD authentication to Amazon RDS for Db2 in another account.
To join an RDS instance across accounts, several high-level steps include creating and sharing an AWS Managed Microsoft AD directory, setting up the networking environment and creating an RDS for Db2 instance to domain-join the shared directory.
AWS Managed AD directory sharing between AWS accounts requires proper network setup comprising information such as VPC ID and AWS account ID of requester and accepter accounts, and identifying subnets to use while creating a peering connection between two VPCs with different CIDR address ranges.
AWS offers several methods to connect two or more VPCs such as VPC peering, AWS Transit Gateway, AWS Private Link, a VPN connection, AWS Direct Connect, a Load Balancer and a Shared VPC. Users can choose a method appropriate for their requirements.
The final steps involved in joining RDS across accounts include editing DNS settings, editing route tables of VPCs, adding a route in the security group, and testing connectivity between the two accounts.
To get a shared directory name, users can use the AWS CLI command 'aws ds describe-directories' and create an IAM Role named AmazonRDSDirectoryServiceRole through the CLI.
To connect users and groups with or without Kerberos authentication, users can use the same AWS Managed Microsoft AD directory to serve multiple accounts.
This post was authored by Vikram S Khatri, Kanda Zhang, Sumit Kumar, and Vikrant Dhir from Amazon Web Services.

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Siliconangle

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SkySQL nabs $6.6M for its AI-integrated cloud database

SkySQL, a cloud-based relational database startup, has raised $6.6 million in seed funding.
The funding was provided by Eniac Ventures, Good Capital, and WTI.
SkySQL offers two editions of its cloud service - Provisioned Mode and Serverless Mode.
The company also provides AI features, including Semantic Agents and SkyCopilot, to make its database service easier to use.

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Amazon

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Scaling to 70M users: How Flo Health optimized Amazon DynamoDB for cost and performance

Flo Health, a female health app with over 70 million monthly active users, has explained best practices for operating a large-scale app, optimized using Amazon DynamoDB, and achieving 60% cost efficiency.
To build a reliable and highly scalable database, DynamoDB was chosen to support the dynamic, read-heavy workload required for the 'Stories' feature, designed to curate interactive health education programs for women's health and improve user engagement.
Flo Health utilized AWS Well-Architected Framework (WAL) by performing an assessment of DynamoDB implementation, having an understanding of capacity mode analysis, and utilization of reserved capacity.
The company reduced costs by 20% by implementing Time-to-Live (TTL) and using reserved capacity, which reduced Write Capacity Units (WCU) usage by threefold.
Flo's data optimizations included datatype size optimization, whereby using numeric datatype instead of string datatype reduced the size of attribute across millions of items leading to saving substantial storage.
Flo also switched from string story ID to numeric ID leading to nine times more compact data storage, and implemented TTL for archiving or automatically expiring data that was no longer needed.
To enhance system efficiency and reduce costs, Flo implemented two key access optimizations: dirty checking and group update requests.
Overall, Flo's optimization of DynamoDB implementation improved system performance and cost efficiency, reducing provisioned WCU by 60%, lowering write activity and sustaining scalability.
By utilizing the AWS Well-Architected-Lens, similar results can be replicated to optimize cloud environments for scalability and cost-efficiency.
Flo Health's technical team is composed of Aleksej Klebanskij, Maksim Koutun, Maksim Ponasov, Lee Hannigan, and Mladen Trampic.

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Dbi-Services

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JSON in PostgreSQL : a query tuning case

ORMs can sometimes produce overly complex SQL with excessive joins and business logic which makes tuning queries difficult. JSON data types add another layer of complexity for DBAs to manage, often leading to full table scans and inefficient execution plans. Improving ORM-generated queries requires limiting dataset early in the query execution with INNER JOINs, adding indexing for frequent field accesses, simplifying WHERE clause conditions by eliminating functions on JSON fields, and reducing buffer operations. PostgreSQL allows creating different types of indexes for JSON and JSONB fields, such as GIN indexes or BTREE indexes.
Avoiding unnecessary casting of JSON data is important to prevent the optimizer from degrading performance. Leveraging JSON path queries is a more advanced querying capability that PostgreSQL 12 introduced for more complex JSON structures. Views or materialized views can be used to extract JSON fields into regular columns, reducing computational burdens during query execution and allowing the database optimizer to use indexes more effectively. Best practices for JSON include using JSON data types when schema flexibility is required and storing semi-structured or unstructured data, embedding related data and aggregating diverse data. A relational schema should be used when data integrity is crucial, frequent updates on specific fields are necessary, complex queries and reporting are required, and performance optimization is paramount.
Understanding how to use JSONB and PostgreSQL effectively can bring developers and DBAs closer to designing flexible, efficient, and scalable applications that combine the flexibility of JSON data types with the reliability of a relational database.

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VoltDB

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These 7 Edge Data Challenges Will Test Companies the Most in 2025

Edge computing has transformed how businesses and industries process and manage data.
The 7 most pressing edge data-related challenges coming in 2025 are data security, data overload and storage limitations, real-time data processing bottlenecks, interoperability between edge devices, data sovereignty and compliance, sustainability in edge deployments, and talent and expertise shortages.
To address these challenges, organizations should adopt a zero-trust security framework, employ data reduction techniques and tiered storage systems, optimize edge workloads, adopt open standards and APIs, implement edge governance policies, invest in energy-efficient edge devices, and build talent pipelines.
Organizations that proactively address these hurdles will be well-positioned to unlock the full potential of edge technology in the years to come.

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Mysql

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Some MySQL Router 8.4.3 read-write splitting quips

Bug 116950: Router Read-Write splitting config causes PHP RSET_HEADER error
Bug 116951: mysqlrouter Error parsing stats_updates_frequency errors
Bug 116952: Router w/ RW split causes ERROR: 4501 if sql script contains comments lines (“– “)
The author encountered issues with MySQL Router v8.4.3 and read-write splitting, leading to the discovery of several bugs, including PHP RSET_HEADER error, error parsing stats_updates_frequency, and an error when executing SQL scripts containing comment lines with double dashed (--). The bugs have been reported and suggested fixes have been provided.

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Dev

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Mastering MySQL's Query Cache: Key Variables and Best Practices for Optimization

MySQL Query Cache can cache the results of SELECT queries to improve database performance.
Query Cache avoids the time-consuming steps of query processing and disk access by leveraging the memory access speed.
The 'query_cache_type' variable controls whether the query cache is enabled, disabled, or set to operate only on demand.
The 'query_cache_size' variable determines the amount of memory allocated for the query cache.
The 'query_cache_limit' variable sets the maximum size for individual query results that can be cached.
The 'query_cache_min_res_unit' variable determines the minimum size in bytes for blocks allocated by the query cache.
It is important to consider factors such as query patterns, cache invalidation, concurrency, and MySQL version when adjusting cache settings.
The QCache Fragmentation and QcacheDeleteRate can be used to monitor the Query Cache's performance.
Adjusting the Query Cache size and optimizing queries can help reduce Qcache Fragmentation and QcacheDeleteRate.
Tools like Releem can simplify the process of optimizing Query Cache settings by automating adjustments based on actual system load and performance.

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Insider

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Will the world's fastest supercomputer please stand up?

Companies like Oracle and xAI are claiming to have the world's largest and fastest supercomputers, making it difficult to determine the true leader.
Supercomputing power is not solely determined by cluster size, but also factors like networking, programming, and power efficiency.
Nvidia's largest customers, Meta, Microsoft, and xAI, likely have the most powerful clusters.
The race to build more advanced models requires a combination of compute power and efficiency.

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Dbi-Services

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FreeBSD basics – 6 – The ports collection

FreeBSD has an official alternative to getting additional software through the Ports Collection.
Ports are source code that come with all the patches and Makefiles required to compile and install a specific program on FreeBSD.
There are thousands of ports in the directories that may become time-consuming to browse through. You can use tools like 'whereis' or build the index file using the make command to search for a port.
The PostgreSQL 17 can be installed by using the Ports Collection. Enter the ports directory, make install and let it build and install. PostgreSQL sources will then begin to download, and you will be asked various questions.
Once it is done, you'll find the PostgreSQL binaries in /usr/local/bin/. All the questions answered can also be given on the command line to automate the process.
A lot of additional stuff is available in the ports collection. If something is not available as a package, have a look there.
If you want to see what’s going in the ports collection, have a look at FreshPorts.

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The New Stack

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Scale Data Platforms With a Kubernetes-First Approach

A data platform is the core of modern data-driven organizations, enabling the integration, management and analysis of data at scale.
Kubernetes has become the key enabler of these architectures by efficiently orchestrating containers.
Kubernetes operators make database management on Kubernetes a viable and even superior option.
Legacy deployment of databases involves a manual, error-prone process, whereas Kubernetes offers automation and orchestration.
Running data platforms inside Kubernetes offers advantages such as simplified deployment and unified management.
Kubernetes’ self-healing capabilities make it possible for databases to recover from failures with no downtime.
Operators enable organizations to build their own self-healing, automated data platforms and offer greater flexibility and cost savings.
Kubernetes provides cloud portability and control over essential features such as automated scaling, backup and monitoring.
Running databases on Kubernetes is not the right fit in every situation and requires careful assessment of the trade-offs.
Kubernetes provides a more automated, cost-effective and future-proof solution for managing databases.

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Dev

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Everyone Uses Postgres… But Why?

PostgreSQL is the most popular database among companies and developers, according to Stack Overflow Developer Survey 2023. Many big companies use PostgreSQL in their day-to-day operations, including Instagram, Reddit, and Twitch.
PostgreSQL was one of the first relational database management systems and has been continuously developed since 1973. It has strong foundations and is enterprise-ready, supporting features like indexes, security, replication, columnar storage, isolation levels, partitioning, encryption, OLAP, cloud deployment, and vendor lock-in.
PostgreSQL also supports many unique things like its extension mechanism which allows anyone to extend it to provide more features. It can be easily adapted to build highly tailored solutions and make it easy to adopt new technologies.
PostgreSQL is capable of storing different types of data, can deal with complex data such as spatial information, offers full-text search (FTS) with various extensions, and supports analytical processes, time series, and more. It has an extension mechanism that empowers it with virtually limitless functionality and can support any type of workload.
Instead of building a new database from scratch, we can just extend PostgreSQL with new capabilities and let it deal with the hard part of optimization, security, user management, and other elements that every production-grade system must have. When some new requirement comes into play, we don’t need to start from scratch but we just need to extend PostgreSQL with an extension.
PostgreSQL supports enterprise-level requirements around HA, scalability, permissions, or security. This versatility makes PostgreSQL an exceptionally adaptable database capable of meeting a wide range of requirements and explains why it’s now the most popular database in the world.

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Bigdataanalyticsnews

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The Role of Databases in Modern Data Management

Databases play a crucial role in modern data management, enabling efficient storage, retrieval, and management of information.
Data quality tools are essential for maintaining reliable data within databases, ensuring accuracy and consistency.
High-quality data is essential for effective data-driven decision-making, enabling businesses to generate insights and make informed choices.
Implementing a robust data quality strategy involves clear objectives, investing in the right tools, establishing data governance, and promoting a data-driven culture.

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