In this tutorial, we’ll go over how to use and understand React Hooks. This article is an extension of article how-to-manage-state-with-hooks-on-react-components. It has been expanded with other Hooks and logic and Lessons Learned.
Here we create a simple product page with a shopping cart (see image 2). The shopping cart represents the memory (or the ‘state’) of the product page. The state generally refers to application data that must be tracked.

Efficient code doesn’t just run faster; if it’s using less compute-resource, it may also be cheaper to run. In particular, distributed cloud applications can benefit from fast, lightweight serialization. 
OpenSource Java Serializer
Chronicle-Wire is an OpenSource Java serializer that can read and write to different message formats such as JSON, YAML, and raw binary data. This serializer can find a middle ground between compacting data formatting (storing more data in the same space) versus compressing data (reducing the amount of storage required). Instead, data is stored in as few bytes as possible without causing performance degradation. This is done through marshaling an object.

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Storing data at rest for reporting and analytics requires different capabilities and SLAs than continuously processing data in motion for real-time workloads. Many open-source frameworks, commercial products, and SaaS cloud services exist. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. Learn how to build a modern data stack with cloud-native technologies. This is part 5: Best Practices for Building a Cloud-Native Data Warehouse or Data Lake.
Blog Series: Data Warehouse vs. Data Lake vs. Data Streaming
This blog series explores concepts, features, and trade-offs of a modern data stack using a data warehouse, data lake, and data streaming together:

Embedding a microservice into another means that a microservice can be run in the same execution context as a parent microservice. Such a mechanism is natively available in Jolie, a service-oriented programming language. Since it is not a native mechanism available in mainstream technologies, it is not widely used. But it allows dealing with microservices from a new and different perspective that opens up new ways for composing them. In this article, I will explain why.
What Is Embedding?
In the following diagram, the main concept behind embedding is reported.

In today’s data-rich and fast-paced business environment, it is quite evident that an organization’s success heavily relies on data insights that embedded analytics software can provide them with.
With this in mind, choosing the right embedded analytics software to integrate into your applications is crucial, and I know that is not an easy task. To help you narrow your search and better evaluate different software solutions that you might consider partnering with, I made this list of the 5 must-have embedded analytics capabilities so that you can make the best decision for your business.

I remember my first day as a junior dev. It’s still fresh in my mind like it was yesterday. I was terribly nervous and had no idea what I was doing. My anxiety must have been evident because a kind soul decided to take me under their wing. That day I learned how to write SQL in my PHP code to do interesting things with the database.
Before I could start, though, I had to ask the database administrator (DBA) to create a few tables. I quickly realized that the DBA was the go-to person if you wanted to get anything done. Need a new column? Call the DBA. Does a stored procedure have to be edited? It was a job for the DBA. I looked up to him. He was such a superstar that I went on to be a DBA myself for a spell later in my career.

While Electronic Data Interchange (EDI) has been extensively used since the late 1960s, there are many companies that rely on legacy systems for streamlining B2B transactions. Legacy B2B transactions, such as Purchase Orders, Invoices, Advance Ship notices, Sales Orders, and Functional Acknowledgement receipts, encompass a set of steps to process. In order to process these business transactions, companies need access to multiple paper documents and a lot of human intervention, which makes them prone to mistakes and risky errors. However, with the support of EDI, the use of paper documents is almost eliminated. 
EDI empowers companies to automate data exchange between different applications across supply chain ecosystems. This particular process ensures that complex, bi-directional data is transferred and exchanged on time. According to a market report, the market size was expected to reach $2.1 billion by 2018 and $2.1 billion by 2020. But what are the benefits of EDI over legacy solutions of business communication and data exchange? 

Technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have led the way to software robots that reduce the manual, time-consuming, and repetitive actions performed on digital platforms. The concept of automating tasks on digital platforms is called robotic process automation (RPA). RPA is a software robot that interacts with computer-centric processes and aims to introduce a digital workforce that performs repetitive tasks previously completed by humans. This Refcard introduces RPA technology, how it works, key components, and how to set up your environment.

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