#OLTP

[govindhtech]

5th Gen Intel Xeon Scalable Processors Boost SQL Server 2022

5th Gen Intel Xeon Scalable Processors

While speed and scalability have always been essential to databases, contemporary databases also need to serve AI and ML applications at higher performance levels. Real-time decision-making, which is now far more widespread, should be made possible by databases together with increasingly faster searches. Databases and the infrastructure that powers them are usually the first business goals that need to be modernized in order to support analytics. The substantial speed benefits of utilizing 5th Gen Intel Xeon Scalable Processors to run SQL Server 2022 will be demonstrated in this post.

OLTP/OLAP Performance Improvements with 5th gen Intel Xeon Scalable processors

The HammerDB benchmark uses New Orders per minute (NOPM) throughput to quantify OLTP. Figure 1 illustrates performance gains of up to 48.1% NOPM Online Analytical Processing when comparing 5th Gen Intel Xeon processors to 4th Gen Intel Xeon processors, while displays up to 50.6% faster queries.

The enhanced CPU efficiency of the 5th gen Intel Xeon processors, demonstrated by its 83% OLTP and 75% OLAP utilization, is another advantage. When compared to the 5th generation of Intel Xeon processors, the prior generation requires 16% more CPU resources for the OLTP workload and 13% more for the OLAP workload.

The Value of Faster Backups

Faster backups improve uptime, simplify data administration, and enhance security, among other things. Up to 2.72x and 3.42 quicker backups for idle and peak loads, respectively, are possible when running SQL Server 2022 Enterprise Edition on an Intel Xeon Platinum processor when using Intel QAT.

The reason for the longest Intel QAT values for 5th Gen Intel Xeon Scalable Processors is because the Gold version includes less backup cores than the Platinum model, which provides some perspective for the comparisons.

With an emphasis on attaining near-real-time latencies, optimizing query speed, and delivering the full potential of scalable warehouse systems, SQL Server 2022 offers a number of new features. It’s even better when it runs on 5th gen Intel Xeon Processors.

Solution snapshot for SQL Server 2022 running on 4th generation Intel Xeon Scalable CPUs. performance, security, and current data platform that lead the industry.

SQL Server 2022

The performance and dependability of 5th Gen Intel Xeon Scalable Processors, which are well known, can greatly increase your SQL Server 2022 database.

The following tutorial will examine crucial elements and tactics to maximize your setup:

Hardware Points to Consider

Choose a processor: Choose Intel Xeon with many cores and fast clock speeds. Choose models with Intel Turbo Boost and Intel Hyper-Threading Technology for greater performance.

Memory: Have enough RAM for your database size and workload. Sufficient RAM enhances query performance and lowers disk I/O.

Storage: To reduce I/O bottlenecks, choose high-performance storage options like SSDs or fast HDDs with RAID setups.

Modification of Software

Database Design: Make sure your query execution plans, indexes, and database schema are optimized. To guarantee effective data access, evaluate and improve your design on a regular basis.

Configuration Settings: Match your workload and hardware capabilities with the SQL Server 2022 configuration options, such as maximum worker threads, maximum server RAM, and I/O priority.

Query tuning: To find performance bottlenecks and improve queries, use programs like Management Studio or SQL Server Profiler. Think about methods such as parameterization, indexing, and query hints.

Features Exclusive to Intel

Use Intel Turbo Boost Technology to dynamically raise clock speeds for high-demanding tasks.

With Intel Hyper-Threading Technology, you may run many threads on a single core, which improves performance.

Intel QuickAssist Technology (QAT): Enhance database performance by speeding up encryption and compression/decompression operations.

Optimization of Workload

Workload balancing: To prevent resource congestion, divide workloads among several instances or servers.

Partitioning: To improve efficiency and management, split up huge tables into smaller sections.

Indexing: To expedite the retrieval of data, create the proper indexes. Columnstore indexes are a good option for workloads involving analysis.

Observation and Adjustment

Performance monitoring: Track key performance indicators (KPIs) and pinpoint areas for improvement with tools like SQL Server Performance Monitor.

Frequent Tuning: Keep an eye on and adjust your database on a regular basis to accommodate shifting hardware requirements and workloads.

SQL Server 2022 Pricing

SQL Server 2022 cost depends on edition and licensing model. SQL Server 2022 has three main editions:

SQL Server 2022 Standard

Description: For small to medium organizations with minimal database functions for data and application management.

Licensing

Cost per core: ~$3,586.

Server + CAL (Client Access License): ~$931 per server, ~$209 per CAL.

Basic data management, analytics, reporting, integration, and little virtualization.

SQL Server 2022 Enterprise

Designed for large companies with significant workloads, extensive features, and scalability and performance needs.

Licensing

Cost per core: ~$13,748.

High-availability, in-memory performance, business intelligence, machine learning, and infinite virtualization.

SQL Server 2022 Express

Use: Free, lightweight edition for tiny applications, learning, and testing.

License: Free.

Features: Basic capability, 10 GB databases, restricted memory and CPU.

Models for licensing

Per Core: Recommended for big, high-demand situations with processor core-based licensing.

Server + CAL (Client Access License): For smaller environments, each server needs a license and each connecting user/device needs a CAL.

In brief

Faster databases can help firms meet their technical and business objectives because they are the main engines for analytics and transactions. Greater business continuity may result from those databases’ faster backups.

Read more on govindhtech.com

[rexavki]

Clickhouse : OLAP vs OLTP ???

[nusaibaakter2779]

如何构建一个适用于特定需求的特殊数据库？

在信息化、数据化高速发展的今天，“数据驱动决策”已成为企业与组织实现精细化运营的核心武器。而不是所有项目都适用通用数据库架构，面对某些行业或项目的特定需求，我们往往需要构建一个“特殊数据库”——一个根据具体场景量身定制的数据存储与管理系统。那么，如何构建一个适用于特定需求的特殊数据库？本文将从规划、设计到落地实施，层层拆解。

一、明确“特殊”的需求是什么？

在开始之前，我们首先要搞清楚：你的数据库为什么不能使用传统方案？所谓“特殊需求”，可能体现在以下几个方面：

数据结构非常复杂或高度非结构化（如医疗图像+病理文本）；

实时性要求极高（如物联网传感数据）；

数据量超大且需高并发访问（如大规模日志系统）；

安全合规要求特殊（如金融/政府行业）；

多维分析能力强（如BI、数据仓库）；

跨地域同步/分布式部署。

只有把这些“特殊性”具体化，后续的架构设计和技术选型才有依据。

二、需求分析与数据建模

任何一个数据库的起点，都是业务需求。

1. 数据实体与关系梳理

确定业务核心对象，如用户、设备、交易、行为、文件等，并画出ER图（实体关系图），识别出一对一、一对多或多对多的连接。

2. 访问场景拆解

分析常用的读写路径。例如：

是读多写少，还是写多读少？

是否需要全文搜索？

是否要支持回溯历史版本？

这些都会直接影响你选择什么类型的数据库。

3. 数据增长与生命周期管理

估算数据增长曲线、存储周期、归档策略等，提前规划容量与性能优化空间。

三、技术选型：不是只有MySQL

根据不同需求，你可以选用不同类型的数据库： 场景推荐数据库类型示例高并发 OLTP关系型数据库PostgreSQL、MySQL日志/追踪系统时序数据库InfluxDB、Prometheus文档类非结构化NoSQL数据库MongoDB、Couchbase分布式存 特殊数据库 储大数据平台Hadoop、ClickHouse搜索服务搜索引擎型Elasticsearch图关系查询图数据库Neo4j、JanusGraph较高隐私要求加密数据库/私有部署Oracle、Self-hosted CockroachDB

⚠️ 记住：没有万能数据库，只有最适合需求的数据库。

四、特殊数据库的设计原则

1. 模块化与可拓展性

即使是为特殊需求���造，也不要写死逻辑。设计数据表结构与接口时保持一定通用性，方便后期扩展字段、表、索引等。

2. 安全性设计优先级靠前

包括权限管理、数据加密、访问审计、接口调用频控等，不管是B端还是C端产品，数据安全都不可妥协。

3. 多副本与灾备机制

关键数据建议启用主从复制或集群同步机制，实现异地容灾、秒级恢复能力，避免数据丢失。

4. 接口友好与自动化运维

为数据库设计一层数据访问API，封装常用的操作逻辑，并接入Prometheus等监控系统，实现告警、备份、自动扩容等。

五、落地实施流程建议

原型设计阶段 使用本地测试数据搭建最小可用数据库结构（MVP），优先验证核心查询与写入逻辑。

性能压力测试 使用 JMeter、sysbench 或自研脚本进行负载模拟，测试系统在高并发、极限数据量下的表现。

安全评估和数据保护 加密传输（HTTPS）、数据库加密字段（如手机号、密码）以及数据脱敏展示应同步推进。

迭代部署上线 采用灰度发布或小流量验证方式，逐步从测试环境过渡到生产环境，避免一次性上线带来风险。

六、真实场景案例分享

场景：智慧医疗影像系统数据库设计

数据特点：图像+病理报告+医生诊断，文件大、种类多、查询路径复杂

解决方案：使用MongoDB存储报告与图像索引，结合S3对象存储挂接实际影像，利用PostgreSQL记录用户行为与审批流程

优化结果：查询响应时间从原有系统的5秒降低至1.2秒，故障率下降90%

七、结语：数据库不是代码，而是“架构能力”的体现

打造一个真正适用于特定需求的特殊数据库，不仅是技术活，更是对业务理解与系统思维的综合体现。只有深入理解问题本质，选对工具、建好结构、管好数据，才能让数据库成为项目的“引擎”而非“负担”。

所以，如果你正面临构建一个“独特场景下”的数据库系统，不妨从这篇文章的步骤开始，一步步走，少走弯路，系统成型后你会发现——它远比看上去更有价值。

[differenttimemachinecrusade]

In-Memory Computing Market Landscape: Opportunities and Competitive Insights 2032

The In-Memory Computing Market was valued at USD 10.9 Billion in 2023 and is expected to reach USD 45.0 Billion by 2032, growing at a CAGR of 17.08% from 2024-2032

The in-memory computing (IMC) market is experiencing rapid expansion, driven by the growing demand for real-time data processing, AI, and big data analytics. Businesses across industries are leveraging IMC to enhance performance, reduce latency, and accelerate decision-making. As digital transformation continues, organizations are adopting IMC solutions to handle complex workloads with unprecedented speed and efficiency.

The in-memory computing market continues to thrive as enterprises seek faster, more scalable, and cost-effective solutions for managing massive data volumes. Traditional disk-based storage systems are being replaced by IMC architectures that leverage RAM, flash memory, and advanced data grid technologies to enable high-speed computing. From financial services and healthcare to retail and manufacturing, industries are embracing IMC to gain a competitive edge in the era of digitalization.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3570 

Market Keyplayers:

SAP SE – SAP HANA

IBM – IBM Db2 with BLU Acceleration

Microsoft – Azure SQL Database In-Memory

Oracle Corporation – Oracle TimesTen In-Memory Database

Intel – Intel Optane DC Persistent Memory

Microsoft – SQL Server In-Memory OLTP

GridGain Systems – GridGain In-Memory Computing Platform

VMware – VMware vSphere with Virtual Volumes

Amazon Web Services (AWS) – Amazon ElastiCache

Pivotal Software – Pivotal GemFire

TIBCO Software Inc.– TIBCO ActiveSpaces

Redis Labs – Redis Enterprise

Hazelcast – Hazelcast IMDG (In-Memory Data Grid)

Cisco – Cisco In-Memory Analytics

Qlik – Qlik Data integration

Market Trends Driving Growth

1. Rising Adoption of AI and Machine Learning

The increasing use of artificial intelligence (AI) and machine learning (ML) applications is fueling the demand for IMC solutions. AI-driven analytics require real-time data processing, making IMC an essential component for businesses leveraging predictive insights and automation.

2. Growing Demand for Real-Time Data Processing

IMC is becoming a critical technology in industries where real-time data insights are essential. Sectors like financial services, fraud detection, e-commerce personalization, and IoT-driven smart applications are benefiting from the high-speed computing capabilities of IMC platforms.

3. Integration with Cloud Computing

Cloud service providers are incorporating in-memory computing to offer faster data processing capabilities for enterprise applications. Cloud-based IMC solutions enable scalability, agility, and cost-efficiency, making them a preferred choice for businesses transitioning to digital-first operations.

4. Increased Adoption in Financial Services

The financial sector is one of the biggest adopters of IMC due to its need for ultra-fast transaction processing, risk analysis, and algorithmic trading. IMC helps banks and financial institutions process vast amounts of data in real time, reducing delays and improving decision-making accuracy.

5. Shift Toward Edge Computing

With the rise of edge computing, IMC is playing a crucial role in enabling real-time data analytics closer to the data source. This trend is particularly significant in IoT applications, autonomous vehicles, and smart manufacturing, where instant processing and low-latency computing are critical.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3570 

Market Segmentation:

By Components

Hardware

Software

Services

By Application

Fraud detection

Risk management

Real-time analytics

High-frequency trading

By Vertical

BFSI

Healthcare

Retail

Telecoms

Market Analysis and Current Landscape

Key factors contributing to this growth include:

Surging demand for low-latency computing: Businesses are prioritizing real-time analytics and instant decision-making to gain a competitive advantage.

Advancements in hardware and memory technologies: Innovations in DRAM, non-volatile memory, and NVMe-based architectures are enhancing IMC capabilities.

Increased data volumes from digital transformation: The exponential growth of data from AI, IoT, and connected devices is driving the need for high-speed computing solutions.

Enterprise-wide adoption of cloud-based IMC solutions: Organizations are leveraging cloud platforms to deploy scalable and cost-efficient IMC architectures.

Despite its strong growth trajectory, the market faces challenges such as high initial investment costs, data security concerns, and the need for skilled professionals to manage and optimize IMC systems.

Regional Analysis: Growth Across Global Markets

1. North America

North America leads the in-memory computing market due to early adoption of advanced technologies, significant investments in AI and big data, and a strong presence of key industry players. The region’s financial services, healthcare, and retail sectors are driving demand for IMC solutions.

2. Europe

Europe is witnessing steady growth in IMC adoption, with enterprises focusing on digital transformation and regulatory compliance. Countries like Germany, the UK, and France are leveraging IMC for high-speed data analytics and AI-driven business intelligence.

3. Asia-Pacific

The Asia-Pacific region is emerging as a high-growth market for IMC, driven by increasing investments in cloud computing, smart cities, and industrial automation. Countries like China, India, and Japan are leading the adoption, particularly in sectors such as fintech, e-commerce, and telecommunications.

4. Latin America and the Middle East

These regions are gradually adopting IMC solutions, particularly in banking, telecommunications, and energy sectors. As digital transformation efforts accelerate, demand for real-time data processing capabilities is expected to rise.

Key Factors Driving Market Growth

Technological Advancements in Memory Computing – Rapid innovations in DRAM, NAND flash, and persistent memory are enhancing the efficiency of IMC solutions.

Growing Need for High-Speed Transaction Processing – Industries like banking and e-commerce require ultra-fast processing to handle large volumes of transactions.

Expansion of AI and Predictive Analytics – AI-driven insights depend on real-time data processing, making IMC an essential component for AI applications.

Shift Toward Cloud-Based and Hybrid Deployments – Enterprises are increasingly adopting cloud and hybrid IMC solutions for better scalability and cost efficiency.

Government Initiatives for Digital Transformation – Public sector investments in smart cities, digital governance, and AI-driven public services are boosting IMC adoption.

Future Prospects: What Lies Ahead?

1. Evolution of Memory Technologies

Innovations in next-generation memory solutions, such as storage-class memory (SCM) and 3D XPoint technology, will further enhance the capabilities of IMC platforms, enabling even faster data processing speeds.

2. Expansion into New Industry Verticals

IMC is expected to witness growing adoption in industries such as healthcare (for real-time patient monitoring), logistics (for supply chain optimization), and telecommunications (for 5G network management).

3. AI-Driven Automation and Self-Learning Systems

As AI becomes more sophisticated, IMC will play a key role in enabling real-time data processing for self-learning AI models, enhancing automation and decision-making accuracy.

4. Increased Focus on Data Security and Compliance

With growing concerns about data privacy and cybersecurity, IMC providers will integrate advanced encryption, access control, and compliance frameworks to ensure secure real-time processing.

5. Greater Adoption of Edge Computing and IoT

IMC’s role in edge computing will expand, supporting real-time data processing in autonomous vehicles, smart grids, and connected devices, driving efficiency across multiple industries.

Access Complete Report: https://www.snsinsider.com/reports/in-memory-computing-market-3570 

Conclusion

The in-memory computing market is witnessing rapid expansion as organizations embrace real-time data processing to drive innovation and competitive advantage. With the integration of AI, cloud computing, and edge technologies, IMC is set to revolutionize industries by enabling faster, more efficient decision-making. As advancements in memory technology continue, businesses that invest in IMC solutions will be well-positioned for the future of high-performance computing.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

The In-Memory Computing Market was valued at USD 10.9 Billion in 2023 and is expected to reach USD 45.0 Billion by 2032, growing at a CAGR of 17.08% from 2024-2032

The in-memory computing (IMC) market is experiencing rapid expansion, driven by the growing demand for real-time data processing, AI, and big data analytics. Businesses across industries are leveraging IMC to enhance performance, reduce latency, and accelerate decision-making. As digital transformation continues, organizations are adopting IMC solutions to handle complex workloads with unprecedented speed and efficiency.

The in-memory computing market continues to thrive as enterprises seek faster, more scalable, and cost-effective solutions for managing massive data volumes. Traditional disk-based storage systems are being replaced by IMC architectures that leverage RAM, flash memory, and advanced data grid technologies to enable high-speed computing. From financial services and healthcare to retail and manufacturing, industries are embracing IMC to gain a competitive edge in the era of digitalization.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3570 

Market Keyplayers:

SAP SE – SAP HANA

IBM – IBM Db2 with BLU Acceleration

Microsoft – Azure SQL Database In-Memory

Oracle Corporation – Oracle TimesTen In-Memory Database

Intel – Intel Optane DC Persistent Memory

Microsoft – SQL Server In-Memory OLTP

GridGain Systems – GridGain In-Memory Computing Platform

VMware – VMware vSphere with Virtual Volumes

Amazon Web Services (AWS) – Amazon ElastiCache

Pivotal Software – Pivotal GemFire

TIBCO Software Inc.– TIBCO ActiveSpaces

Redis Labs – Redis Enterprise

Hazelcast – Hazelcast IMDG (In-Memory Data Grid)

Cisco – Cisco In-Memory Analytics

Qlik – Qlik Data integration

Market Trends Driving Growth

1. Rising Adoption of AI and Machine Learning

The increasing use of artificial intelligence (AI) and machine learning (ML) applications is fueling the demand for IMC solutions. AI-driven analytics require real-time data processing, making IMC an essential component for businesses leveraging predictive insights and automation.

2. Growing Demand for Real-Time Data Processing

IMC is becoming a critical technology in industries where real-time data insights are essential. Sectors like financial services, fraud detection, e-commerce personalization, and IoT-driven smart applications are benefiting from the high-speed computing capabilities of IMC platforms.

3. Integration with Cloud Computing

Cloud service providers are incorporating in-memory computing to offer faster data processing capabilities for enterprise applications. Cloud-based IMC solutions enable scalability, agility, and cost-efficiency, making them a preferred choice for businesses transitioning to digital-first operations.

4. Increased Adoption in Financial Services

The financial sector is one of the biggest adopters of IMC due to its need for ultra-fast transaction processing, risk analysis, and algorithmic trading. IMC helps banks and financial institutions process vast amounts of data in real time, reducing delays and improving decision-making accuracy.

5. Shift Toward Edge Computing

With the rise of edge computing, IMC is playing a crucial role in enabling real-time data analytics closer to the data source. This trend is particularly significant in IoT applications, autonomous vehicles, and smart manufacturing, where instant processing and low-latency computing are critical.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3570 

Market Segmentation:

By Components

Hardware

Software

Services

By Application

Fraud detection

Risk management

Real-time analytics

High-frequency trading

By Vertical

BFSI

Healthcare

Retail

Telecoms

Market Analysis and Current Landscape

Key factors contributing to this growth include:

Surging demand for low-latency computing: Businesses are prioritizing real-time analytics and instant decision-making to gain a competitive advantage.

Advancements in hardware and memory technologies: Innovations in DRAM, non-volatile memory, and NVMe-based architectures are enhancing IMC capabilities.

Increased data volumes from digital transformation: The exponential growth of data from AI, IoT, and connected devices is driving the need for high-speed computing solutions.

Enterprise-wide adoption of cloud-based IMC solutions: Organizations are leveraging cloud platforms to deploy scalable and cost-efficient IMC architectures.

Despite its strong growth trajectory, the market faces challenges such as high initial investment costs, data security concerns, and the need for skilled professionals to manage and optimize IMC systems.

Regional Analysis: Growth Across Global Markets

1. North America

North America leads the in-memory computing market due to early adoption of advanced technologies, significant investments in AI and big data, and a strong presence of key industry players. The region’s financial services, healthcare, and retail sectors are driving demand for IMC solutions.

2. Europe

Europe is witnessing steady growth in IMC adoption, with enterprises focusing on digital transformation and regulatory compliance. Countries like Germany, the UK, and France are leveraging IMC for high-speed data analytics and AI-driven business intelligence.

3. Asia-Pacific

The Asia-Pacific region is emerging as a high-growth market for IMC, driven by increasing investments in cloud computing, smart cities, and industrial automation. Countries like China, India, and Japan are leading the adoption, particularly in sectors such as fintech, e-commerce, and telecommunications.

4. Latin America and the Middle East

These regions are gradually adopting IMC solutions, particularly in banking, telecommunications, and energy sectors. As digital transformation efforts accelerate, demand for real-time data processing capabilities is expected to rise.

Key Factors Driving Market Growth

Technological Advancements in Memory Computing – Rapid innovations in DRAM, NAND flash, and persistent memory are enhancing the efficiency of IMC solutions.

Growing Need for High-Speed Transaction Processing – Industries like banking and e-commerce require ultra-fast processing to handle large volumes of transactions.

Expansion of AI and Predictive Analytics – AI-driven insights depend on real-time data processing, making IMC an essential component for AI applications.

Shift Toward Cloud-Based and Hybrid Deployments – Enterprises are increasingly adopting cloud and hybrid IMC solutions for better scalability and cost efficiency.

Government Initiatives for Digital Transformation – Public sector investments in smart cities, digital governance, and AI-driven public services are boosting IMC adoption.

Future Prospects: What Lies Ahead?

1. Evolution of Memory Technologies

Innovations in next-generation memory solutions, such as storage-class memory (SCM) and 3D XPoint technology, will further enhance the capabilities of IMC platforms, enabling even faster data processing speeds.

2. Expansion into New Industry Verticals

IMC is expected to witness growing adoption in industries such as healthcare (for real-time patient monitoring), logistics (for supply chain optimization), and telecommunications (for 5G network management).

3. AI-Driven Automation and Self-Learning Systems

As AI becomes more sophisticated, IMC will play a key role in enabling real-time data processing for self-learning AI models, enhancing automation and decision-making accuracy.

4. Increased Focus on Data Security and Compliance

With growing concerns about data privacy and cybersecurity, IMC providers will integrate advanced encryption, access control, and compliance frameworks to ensure secure real-time processing.

5. Greater Adoption of Edge Computing and IoT

IMC’s role in edge computing will expand, supporting real-time data processing in autonomous vehicles, smart grids, and connected devices, driving efficiency across multiple industries.

Access Complete Report: https://www.snsinsider.com/reports/in-memory-computing-market-3570 

Conclusion

The in-memory computing market is witnessing rapid expansion as organizations embrace real-time data processing to drive innovation and competitive advantage. With the integration of AI, cloud computing, and edge technologies, IMC is set to revolutionize industries by enabling faster, more efficient decision-making. As advancements in memory technology continue, businesses that invest in IMC solutions will be well-positioned for the future of high-performance computing.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[rajaniesh]

🚀 Struggling to balance transactional (OLTP) & analytical (OLAP) workloads? Microsoft Fabric SQL Database is the game-changer! In this blog, I’ll share best practices, pitfalls to avoid, and optimization tips to help you master Fabric SQL DB. Let’s dive in! 💡💬 #MicrosoftFabric #SQL

[kaobei-engineer]

#純靠北工程師8gk

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有時候啊，工作環境變糟糕，真的是自己活該找死的。 譬如說我曾經和講得出 OLAP / OLTP 的同事共事，但是我覺得做內部系統太無聊就離開了。 然後現在的同事特別愛用 MariaDB，但是連有正規化這件事都不知道， 資料表設計和業務邏輯程式的寫法，則是把 SQL 當 BigQuery 來用這麼的糟糕。

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💖 純靠北工程師 官方 Discord 歡迎在這找到你的同溫層！

👉 https://discord.gg/tPhnrs2

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💖 全平台留言、文章詳細內容

👉 https://init.engineer/cards/show/10964

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典型的なアプリケーションは、OLTPトランザクション量が大きいことが特徴である。OLTPトランザクションは、 ・短時間で終了し（つまり、ユーザーの応答待ち（ユーザーストール）がない）、 ・トランザクションごとに少量のデータにアクセスし、 ・インデックス付きのルックアップ（テーブルスキャンなし）を使用し、 ・少数のフォーム（異なる引数を持つ少数のクエリー）を持つ。 しかし、中にはハイブリッドトランザクション/分析処理（英語版）（HTAP）アプリケーションをサポートするものもある。このようなシステムでは、大規模なリカバリ（英語版）や同時実行制御を省略することで、性能とスケーラビリティを向上させる。

NewSQL - Wikipedia

[modulesap]

SAP HANA (High-Performance Analytic Appliance) is an in-memory, column-oriented, relational database management system developed by SAP. Its primary purpose is to provide real-time processing and high-speed analytics for transactional and analytical workloads.

Key Purposes of SAP HANA:

Real-Time Data Processing:Stores and processes large amounts of data in memory (RAM) instead of traditional disk storage, enabling faster read/write operations.

High-Speed Analytics & Reporting:Columnar storage and parallel processing allow instant access to insights, making it ideal for real-time analytics.

Simplified IT Landscape:Eliminates the need for separate OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) systems by allowing both transactional and analytical operations on the same database.

Advanced Data Management & Processing:Supports structured and unstructured data, including text, spatial, and graph data.Includes built-in machine learning and predictive analytics.

Cloud and On-Premise Deployment:Can be deployed on-premise, in the cloud, or in a hybrid environment for flexibility.

Better Performance for SAP Applications:Used as the core database for SAP S/4HANA, improving the speed and efficiency of SAP ERP processes.

Since you're in the SAP SD stream and considering SAP ABAP, knowing HANA is beneficial, as ABAP on HANA is different from traditional ABAP due to optimized coding techniques like CDS views, AMDPs, and HANA-native SQL scripting.

Mail us on contact@anubhavtrainings.com

Website: Anubhav Online Trainings | UI5, Fiori, S/4HANA Trainings

[tekinvaderz]

In today’s data-driven world, effective decision-making relies on different types of data processing to manage and analyze information efficiently. Batch processing is ideal for handling large data sets periodically, while real-time processing provides instant insights for critical decisions. OLTP ensures smooth transaction management, and OLAP allows businesses to uncover trends through multidimensional analysis. Emerging methods like augmented analytics and stream processing leverage AI to automate insights and handle continuous data flows, empowering organizations to stay ahead in a fast-paced environment.

[thanhthienphuvn]

Hướng dẫn download và cài đặt phần mềm SQL Server 2014

Hướng dẫn download và cài đặt phần mềm SQL Server 2014 SQL Server là một hệ quản trị cơ sở dữ liệu (“Relational Database Management System”, viết tắt:

RDBMS) được phát triển bởi Microsoft.

Phần mềm này hỗ trợ người dùng quản lý, phân tích, và khai thác cơ sở dữ liệu một cách hiệu quả.

SQL Server thường được sử dụng hiện tại là phiên bản 2014 có khả năng đạp ứng nhu cầu quản lý dữ liệu cho nhiều doanh nghiệp, từ nhỏ đến lớn, nhờ vào hệ thống tính năng mạnh mẽ, đồng thời hỗ trợ nền tảng Windows.

SQL Server 2014 là một hệ quản trị cơ sở dữ liệu mạnh mẽ, đáp ứng tốt nhu cầu lưu trữ, quản lý và xử lý dữ liệu trong thời đại công nghệ hiện nay.

Với những tính năng nổi bật như In-Memory OLTP, AlwaysOn Availability Groups, và khả năng phân tích dữ liệu tiên tiến, phần mềm này đã trở thành một công cụ không thể thiếu cho các doanh nghiệp và tổ chức.

[ai-news]

#AI #ML #Automation

[govindhtech]

Kingston Launches DC3000ME PCIe 5.0 NVMe U.2 SSD

Kingston Digital Europe Co LLP, the flash memory affiliate of Kingston Technology Company, a global leader in memory products and technical solutions, introduced the DC3000ME PCIe 5.0 NVMe U.2 data centre SSD for server applications today.

The DC3000ME business U.2 SSD offers 3D eTLC NAND and a fast PCIe 5.0 NVMe interface. It is backward compatible with PCIe 4.0 servers and backplanes. The DC3000ME is ideal for AI, HPC, OLTP, cloud services, and edge computing because to its strict QoS requirements for I/O consistency and low latency. The DC3000ME offers AES 256-bit encryption and on-board power loss prevention for maximum data security and power-outage protection.

Kingston EMEA SSD business manager Tony Hollingsbee says, “DC3000ME was designed to provide leading edge performance and ensure predictable random I/O performance as well as predictable latencies over a wide range of server workloads.” A key architectural element of Gen4 and Gen5 NVMe servers for cloud service providers, hyperscale data centres, and system integrators.

The DC3000ME has a five-year warranty, free tech support, and Kingston reliability. The capacities are 3.84TB, 7.68TB, and 15.36TB.

DC3000ME U.2 PCIe 5.0 SSD

Enterprise-grade Gen5 NVMe U.2 SSD for server applications with power outage protection

Kingston's DC3000ME U.2 data centre SSD is perfect for AI, HPC, OLTP, databases, cloud infrastructure, and edge computing due to its 3D eTLC NAND and fast PCIe 5.0 NVMe interface. If the DC3000ME loses power, AES 256-bit encryption and on-board power loss protection safeguard data. The DC3000ME supports PCIe 4.0 servers and backplanes with new PCIe 5.0 interface. The DC3000ME addresses low latency and I/O consistency requirements for cloud service providers, hyperscale data centres, and system integrators. Kingston's legendary technical support and a 5-year guarantee cover the DC3000ME. It has 3.84TB, 7.68TB, and 15.36TB capacities.

Fast enterprise PCIe 5.0

Ideal storage and efficiency

Power loss protection onboard

AES 256-bit security

Workloads and applications

The DC3000ME is ideal for many server workloads and applications:

AI

HPC

Cloud-based services

Edge computing

Software-defined storage

RAID

Server use typically

Important Features

Enterprise PCIe 5.0 performance

Maintains 14,000 MB/s read and 2,800,000 read IOPS with I/O consistency and low latency.

Best storage and efficiency

High-capacity options offer excellent performance and I/O reliability. built to handle various server workloads.

Onboard power loss protection

Enterprise-class data security such end-to-end data protection, TCG Opal 2.0, and NVMe-MI 1.2b out-of-band management reduces data loss or corruption during unscheduled power outages.

256-bit AES

TCG Opal 2.0 and AES 256-bit hardware-based encryption secure critical data.

[helicalinsight]

Seamlessly MySQL to Redshift Migration with Ask On Data

MySQL to Redshift migration is a critical component for businesses looking to scale their data infrastructure. As organizations grow, they often need to transition from traditional relational databases like MySQL to more powerful cloud data warehouses like Amazon Redshift to handle larger datasets, improve performance, and enable real-time analytics. The migration process can be complex, but with the right tools, it becomes much more manageable. Ask On Data is a tool designed to streamline the data wrangling and migration process, helping businesses move from MySQL to Redshift effortlessly.

Why Migrate from MySQL to Redshift?

MySQL, a widely-used relational database management system (RDBMS), is excellent for managing structured data, especially for small to medium-sized applications. However, as the volume of data increases, MySQL can struggle with performance and scalability. This is where Amazon Redshift, a fully managed cloud-based data warehouse, comes into play. Redshift offers powerful query performance, massive scalability, and robust integration with other AWS services.

Redshift is built specifically for analytics, and it supports parallel processing, which enables faster query execution on large datasets. The transition from MySQL to Redshift allows businesses to run complex queries, gain insights from large volumes of data, and perform advanced analytics without compromising performance.

The Migration Process: Challenges and Solutions

Migrating from MySQL to Redshift is not a one-click operation. It requires careful planning, data transformation, and validation. Some of the primary challenges include:

Data Compatibility: MySQL and Redshift have different data models and structures. MySQL is an OLTP (Online Transaction Processing) system optimized for transactional queries, while Redshift is an OLAP (Online Analytical Processing) system optimized for read-heavy, analytical queries. The differences in how data is stored, indexed, and accessed must be addressed during migration.

Data Transformation: MySQL’s schema may need to be restructured to fit Redshift’s columnar storage format. Data types and table structures may also need adjustments, as Redshift uses specific data types optimized for analytical workloads.

Data Volume: Moving large volumes of data from MySQL to Redshift can take time and resources. A well-thought-out migration strategy is essential to minimize downtime and ensure the integrity of the data.

Testing and Validation: Post-migration, it is crucial to test and validate the data to ensure everything is accurately transferred, and the queries in Redshift return the expected results.

How Ask On Data Eases the Migration Process

Ask On Data is a powerful tool designed to assist with data wrangling and migration tasks. The tool simplifies the complex process of transitioning from MySQL to Redshift by offering several key features:

Data Preparation and Wrangling: Before migration, data often needs cleaning and transformation. Ask On Data makes it easy to prepare your data by handling missing values, eliminating duplicates, and ensuring consistency across datasets. It also provides automated data profiling to ensure data quality before migration.

Schema Mapping and Transformation: Ask On Data supports schema mapping, helping you seamlessly convert MySQL schemas into Redshift-compatible structures. The tool automatically maps data types, handles column transformations, and generates the necessary scripts to create tables in Redshift.

Efficient Data Loading: Ask On Data simplifies the process of transferring large volumes of data from MySQL to Redshift. With support for bulk data loading and parallel processing, the tool ensures that the migration happens swiftly with minimal impact on production systems.

Error Handling and Monitoring: Migration can be prone to errors, especially when dealing with large datasets. Ask On Data offers built-in error handling and monitoring features to track the progress of the migration and troubleshoot any issues that arise.

Post-Migration Validation: Once the migration is complete, Ask On Data helps validate the data by comparing the original data in MySQL with the migrated data in Redshift. It ensures that data integrity is maintained and that all queries return accurate results.

Conclusion

Migrating from MySQL to Redshift can significantly improve the performance and scalability of your data infrastructure. While the migration process can be complex, tools like Ask On Data can simplify it by automating many of the steps involved. From data wrangling to schema transformation and data validation, Ask On Data provides a comprehensive solution for seamless migration. By leveraging this tool, businesses can focus on analyzing their data, rather than getting bogged down in the technicalities of migration, ensuring a smooth and efficient transition to Redshift.

[jcineas14]

Reflection on the Business Intelligence Technologies Course

The Business Intelligence Technologies course has been a vital step in achieving the expectations and goals outlined in my Mastery Journal Timeline. My primary focus for this course was to gain practical skills in designing and managing data warehouses while understanding the broader scope of BI systems. This course delivered exactly what I needed by covering key topics like data warehouse architecture, ETL processes, and BI system operations. Each lesson built on these concepts, aligning perfectly with my timeline goals for mastering data management technologies. The hands-on assignments and real-world examples further enriched my understanding by connecting theoretical knowledge to practical applications.

Throughout the course, I gained a comprehensive understanding of how BI technologies support business operations. One of the most valuable insights was learning how data warehouses, data cubes, and OLAP tools work together to enable efficient reporting, performance monitoring, and forecasting. The section on ETL processes was especially impactful, as it demonstrated the importance of data accuracy and consistency in delivering reliable BI insights. Additionally, exploring transactional databases and OLTP systems gave me a clearer view of how data flows and supports daily operations within organizations. The case analyses and statistical tools introduced in the course also helped me see how BI can address real-world business challenges and drive decision-making.

As I continue in the Business Intelligence program, I plan to apply these concepts in my future coursework and Capstone project. The skills I’ve developed in designing and implementing BI systems will be crucial for tackling complex problems and creating actionable solutions. Professionally, this knowledge will empower me to contribute meaningfully to organizations by managing data effectively and using BI tools to uncover insights that improve decision-making and performance. This course has strengthened both my technical skills and my confidence as I move closer to my career goals.

#BI #MasteryJournal #Masters

[verside]

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Important Data Processing System (OLTP vs OLAP)

Not all databases are the same, there are different types of databases for specific workloads, let's understand two of them.

📍 OLTP (Online Transactional Processing):

Processing large volumes of small, individual transactions in real-time, such as bank transactions

📍 OLAP (Online Analytical Processing):

Analyze large volumes of data to support BI such as forecasting

Almost all OLTP systems are row-based, and all of the data are stored row by row.

So when you query any data, it will pull the entire row, even if you just select one column.

So pulling one column = scanning the entire row and then selecting the column

Not efficient!

I made the same mistake early in my career, I ran an Analytics query to get sum/avg on millions of rows

The database server size was tiny and took everything down.

OLTP Examples: MySQL, PostgreSQL, Oracle

On the other hand, the OLAP system is mainly column-based.

So instead of pulling all of the columns, it will only pull columns that are required for analysis.

Specially designed for analysis work.

OLAP Examples: BigQuery, Redshift, Snowflake

[sap001]

What are the differences between SAP HANA and SAP MDG?

SAP HANA (High-Performance Analytic Appliance) and SAP MDG (Master Data Governance) serve different purposes within the SAP ecosystem.

SAP HANA is an in-memory database that allows real-time data processing and analytics. It is primarily used for speeding up transactions and analytics by storing data in-memory rather than on disk, which enhances performance. HANA can support OLAP and OLTP systems, enabling real-time analysis on large datasets.

SAP MDG, on the other hand, focuses on governance, ensuring consistency, and maintaining the quality of master data across an organization. MDG integrates with SAP and non-SAP systems to manage data centrally, ensuring that master data, such as customer or supplier information, is accurate and compliant with business rules.

For those looking to upgrade their SAP skills, Anubhav's online training is highly recommended. Recognized globally, he offers comprehensive courses in SAP, including corporate training. Check out his upcoming courses at Anubhav Trainings.