


The headline of IBM's acquisition of two data platforms from Software AG for €2.13 billion could be rewritten as: IBM completes acquisition of two data platforms from Software AG for €2.13 billion
IBM announced the acquisition of Software AG’s Super iPaaS enterprise technology platform StreamSets and WebMethods for 2.13 billion euros (approximately 16.571 billion yuan) in cash. These products are its core offerings
StreamSets: a cloud-native DataOps and data ingestion platform that helps enterprises achieve unified access and data access to a variety of data sources and types. Delivery, it also facilitates the design of intelligent data pipelines and ingestion of real-time and batch data.
webMethods: An integration and API management platform. The platform can be deployed on-premises or in the cloud, provides B2B integration and managed file transfer capabilities, and provides a modern API gateway to help customers manage, monitor and benefit from APIs

The companies said they expect the deal to close in the second quarter of 2024, pending regulatory approval. IBM said the deal will further strengthen the company's position as demand for customized cloud services for artificial intelligence applications increases
Application and data integration solutions As enterprises and organizations continue to accelerate their digital transformation, It has become a key element for application modernization and even the effective deployment of AI across the entire enterprise. According to IDC, the global integrated software market will exceed US$18 billion by 2027, with a compound annual growth rate of 16.1%
The above is the detailed content of The headline of IBM's acquisition of two data platforms from Software AG for €2.13 billion could be rewritten as: IBM completes acquisition of two data platforms from Software AG for €2.13 billion. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



Big data structure processing skills: Chunking: Break down the data set and process it in chunks to reduce memory consumption. Generator: Generate data items one by one without loading the entire data set, suitable for unlimited data sets. Streaming: Read files or query results line by line, suitable for large files or remote data. External storage: For very large data sets, store the data in a database or NoSQL.

In the Internet era, big data has become a new resource. With the continuous improvement of big data analysis technology, the demand for big data programming has become more and more urgent. As a widely used programming language, C++’s unique advantages in big data programming have become increasingly prominent. Below I will share my practical experience in C++ big data programming. 1. Choosing the appropriate data structure Choosing the appropriate data structure is an important part of writing efficient big data programs. There are a variety of data structures in C++ that we can use, such as arrays, linked lists, trees, hash tables, etc.

AEC/O (Architecture, Engineering & Construction/Operation) refers to the comprehensive services that provide architectural design, engineering design, construction and operation in the construction industry. In 2024, the AEC/O industry faces changing challenges amid technological advancements. This year is expected to see the integration of advanced technologies, heralding a paradigm shift in design, construction and operations. In response to these changes, industries are redefining work processes, adjusting priorities, and enhancing collaboration to adapt to the needs of a rapidly changing world. The following five major trends in the AEC/O industry will become key themes in 2024, recommending it move towards a more integrated, responsive and sustainable future: integrated supply chain, smart manufacturing

1. Background of the Construction of 58 Portraits Platform First of all, I would like to share with you the background of the construction of the 58 Portrait Platform. 1. The traditional thinking of the traditional profiling platform is no longer enough. Building a user profiling platform relies on data warehouse modeling capabilities to integrate data from multiple business lines to build accurate user portraits; it also requires data mining to understand user behavior, interests and needs, and provide algorithms. side capabilities; finally, it also needs to have data platform capabilities to efficiently store, query and share user profile data and provide profile services. The main difference between a self-built business profiling platform and a middle-office profiling platform is that the self-built profiling platform serves a single business line and can be customized on demand; the mid-office platform serves multiple business lines, has complex modeling, and provides more general capabilities. 2.58 User portraits of the background of Zhongtai portrait construction

In today's big data era, data processing and analysis have become an important support for the development of various industries. As a programming language with high development efficiency and superior performance, Go language has gradually attracted attention in the field of big data. However, compared with other languages such as Java and Python, Go language has relatively insufficient support for big data frameworks, which has caused trouble for some developers. This article will explore the main reasons for the lack of big data framework in Go language, propose corresponding solutions, and illustrate it with specific code examples. 1. Go language

As an open source programming language, Go language has gradually received widespread attention and use in recent years. It is favored by programmers for its simplicity, efficiency, and powerful concurrent processing capabilities. In the field of big data processing, the Go language also has strong potential. It can be used to process massive data, optimize performance, and can be well integrated with various big data processing tools and frameworks. In this article, we will introduce some basic concepts and techniques of big data processing in Go language, and show how to use Go language through specific code examples.

IBM suddenly announced a new round of layoffs! IBM's chief communications officer announced the news at the latest seven-minute staff meeting. This time it will mainly focus on the marketing and communications departments. Although IBM did not issue a formal statement on the scale of layoffs, as recently as January this year, they announced that they would lay off 3,400 people. The company's CEO Arvind Krishna said earlier that the company will replace 8,000 jobs with artificial intelligence in the next five years. This whole sudden operation directly confused the employees...Have these 8,000 people started laying off employees long ago△Source: One Acre and Three Minutes According to statistics from relevant websites, about 204 technology companies have laid off employees so far this year Nearly 50,000 people. Including Google parent company Alphabet, Amazon

In big data processing, using an in-memory database (such as Aerospike) can improve the performance of C++ applications because it stores data in computer memory, eliminating disk I/O bottlenecks and significantly increasing data access speeds. Practical cases show that the query speed of using an in-memory database is several orders of magnitude faster than using a hard disk database.
