CodeIsGo.com

At IBM, we obsess over what our clients need—not just to meet today’s business challenges, but what will help them capitalize on future opportunities. By Tina Tarquinio, VP, Product Management, IBM Z and LinuxONE.

The new offerings are built for the modern data center to help optimize flexibility and sustainability, particularly for small- and medium-sized businesses. For IBM Ecosystem partners, the new rack mount configurations open more business opportunities to reach new audiences with specific data center design requirements. Consolidating Linux workloads on an IBM LinuxONE Rockhopper 4 instead of running them on compared x86 servers with similar conditions and location can reduce energy consumption by 75% and space by 67%.

Today’s IT leaders are tasked with not only solving challenging technical problems, but also driving key business outcomes. Digitally transforming operations and reshaping customer experiences requires deep integration across the full stack of technology and business priorities.

IBM provides resources and skills for clients and partners who want to learn more about the new single frame and rack mount configurations and IBM LinuxONE Expert Care. To learn more about the new configurations from industry experts, please attend the upcoming virtual events on April 4 and April 17, 2023. Read the official press release to learn more. Interesting read!

For good reason, the financial services industry is quickly adopting cloud computing technology. Cloud computing provides numerous advantages, including cost savings, scalability, agility, and increased security. By Finance Magnates Staff.

The article explains:

• Cloud computing’s advantages in financial services
• Cloud computing is becoming increasingly popular in financial services
• The challenges of cloud computing in financial services
• The dangers of over relying on cloud computing

The financial services business is being transformed by cloud computing, which offers cost reductions, scalability, agility, and increased security. Financial services firms are increasingly embracing cloud computing technology, such as IaaS, PaaS, and SaaS, to store applications and data on the cloud, develop new apps and services, and access a variety of software applications. Also links to further reading concerning fintech cybersecruity or digital banking trends provided in the blog. Nice one!

Data serialization is a crucial aspect of modern distributed systems because it enables the efficient communication and storage of structured data. In this article, we will discuss two popular serialization formats: Avro and Protocol Buffers, Protobuf for short, and compare their strengths and weaknesses to help you make an informed decision about which one to use in your projects. By Daniel Selans.

Avro is a serialization framework developed by the Apache Software Foundation. It is designed to be language-independent and schema-based, which means that data is serialized and deserialized using a schema that describes the structure of the data. Avro schemas are defined in JSON.

Author then deals with following topics:

• What is data serialization, and why do you need it?
• What is Avro?
• What is Protobuf?
• Avro vs. Protobuf: Strengths and weaknesses

Regardless of the encodings used with streaming or event-driven data, being able to observe, monitor, and act on data is paramount for compliance, reliability, and shipping complex features faster. While Streamdal has first-class support for Protobuf, we work with any encoding to enable total observability and operability on any data stream or event-driven system. Good read!

For those that aren’t familiar, Vulcan is a functional Avro encoding library that uses the official Apache Avro library under the hood. The difference between this and the official Avro build plugins approach is that the types are defined in plain Scala. Then the Avro schema is generated from those instead of defining the Avro schema and getting code generated at compile time that adheres to that schema. By César Enrique.

import vulcan.Codec
import vulcan.generic.*
import java.time.Instant
import java.util.UUID

case class Data(id: UUID, timestamp: Instant, value: String)

object Data:
    given Codec[Data] = Codec.derive[Data]

The obvious benefit for Scala development is that dealing with Java types is no longer needed. Further in the article:

• Vulcan example
• Codec.encode and Codec.decode
• Serializers and deserializers
• Implementing a Serde
• Caveats

To wrap up, we have explored the functionalities provided by Vulcan and implemented a way to integrate it with Kafka APIs that expect Serializer and Deserializer since Vulcan does not support this out of the box.

A data structure which manages resources automatically. It lets us focus on the task at hand by giving us a handle on the acquired resource which is then automatically released in the end so that we avoid resource leaks. By Anzori (Nika) Ghurtchumelia.

The article then describes some useful utilities in Scala:

• PartialFunction[-A, +B]
• scala.util.chaining
• Function types
• Implicits & term inference
• Local functions
• lazy vals & by name parameters
• Type aliases

You will get examples for each mentioned utility. Interesting read!

Android app development is a big and ever-growing industry. Kotlin or Java for Android App Development lets understand more about these technologies. By cumulations.com.

If you are confused about making the right choice between the two, read this article to decide which would be the right to go for. In the article you will get information on:

• Background of Kotlin or Java for Android app development
• Community support
• Extensions
• Performance
• Syntax
• Scalability
• Learning curve
• Smart casts
• Which and when to use?

In the end, the choice of using one over the other will depend on your personal preference; convenience; and the project need. Both have their drawbacks and advantages so you have to be careful in making the selection. Don’t be in hurry or else you will end up messing up everything. Nice one!

In complex and modular Android projects, managing dependencies can be a daunting and time-consuming task. Gradle Version Catalogs, introduced in Gradle 7.0 and promoted to stable in version 7.4, offer an elegant solution to streamline dependency management. In this comprehensive guide, we will explore the benefits of Version Catalogs, demonstrate how to implement them in an Android project with code snippets and provide tips to help you get the most out of this powerful feature. By Kashif Mehmood.

The article then explains why use version catalogs:

• Centralized and shareable configuration: By consolidating dependency coordinates in a single file, Version Catalogs simplify management and promote consistent configuration across multiple projects. 8 Improved performance: Compared to buildSrc, Version Catalogs offer better performance, as updating dependency versions no longer requires a complete rebuild.
• Flexibility: Version Catalogs support the creation of dependency bundles, enabling developers to add a single implementation line for a set of libraries in their Gradle files.
• Version Catalogs support third-party plugins for automatic version updates and offer better performance compared to buildSrc solutions.Type-safe dependencies: Version Catalogs encourage type-safe dependency declarations, reducing typos and improving IDE support for content assistance.

It also provides an example how to implement version catalogs in an Android project with code snippets and configuration. Good read!

Data is the biggest value driver for businesses bringing positive change and enabling near term efficiency. Maintaining an eye on the future is so important to businesses - and it’s why data leadership is of rising value. Although currently there’s a great deal of economic uncertainty, the data community should feel optimistic; this is the opportunity to demonstrate business value, benefit colleagues and play a role in efficiency and sustainability initiatives. By Danielle McConville.

Data leads to answers for those difficult questions business leaders are challenged with. Yet business advisory firm McKinsey finds that data-driven businesses achieve sales growth of between 15 and 25 percent in business-to-business lines.

Few points mentioned in teh article:

• Why have a data strategy
• A better understanding

With the right type of data strategy in place, organisations can leverage a data-driven approach wherein they have a true understanding of where they are currently as a business, where the business needs to go next, and what strategic assets it has to get there. In addition, data can reveal industry trends or disruptions caused by technology or market conditions.

The data opportunity is more than just a short-term efficiency gain. Leading businesses are using data to gain a deeper and more informed understanding of every element of the organisation. McKinsey reports: “data-driven culture fosters continuous performance improvement to create truly differentiated customer and employee experiences.” This is leading to finance optimisation, lean inventory management, customer journey insights and buyer behaviour studies. Good read!

Real-time interchanging data across domains and applications is challenging; data format incompatibility, latency and outdated data sets, quality issues, and lack of metadata and context. A Linked Data Event Stream (LDES) is a new data publishing approach which allows you to publish any dataset as a collection of immutable objects. The focus of an LDES is to allow clients to replicate the history of a dataset and efficiently synchronize with its latest changes. By towardsai.net.

Using Linked Data Event Stream (LDES), data can be fluently shared between different systems and organizations. In this way, companies and organizations can ensure that their data is well-structured, interoperable, and easily consumable by other systems and services. LDES has emerged as a standard for representing and sharing up-to-date data streams.

Further in the article:

• Linked Data Event Streams explained in 8 minutes
• Onboarding of a Linked Data Event Stream
• Interlink multiple Linked Data Event Streams
• SPARQL query
• Combining multiple data streams via their semantics

To replicate the whole proof of concept (LDES 2 GraphDB), please go to provided Github repository. It describes how to set up the GraphDB and Apache NiFi via docker, after which the data flow can be started using the supplied Apache NiFi setup file. Nice one!

HAProxy, ProxySQL, MySQL Router (AKA MySQL Proxy); in the last few years, I had to answer multiple times on what proxy to use and in what scenario. When designing an architecture, many components need to be considered before deciding on the best solution. By Marco Tusa.

When deciding what to pick, there are many things to consider, like where the proxy needs to be, if it “just” needs to redirect the connections, or if more features need to be in, like caching and filtering, or if it needs to be integrated with some MySQL embedded automation.

sysbench ./src/lua/windmills/oltp_read.lua  --db-driver=mysql --tables=200 --table_size=1000000 
 --rand-type=zipfian --rand-zipfian-exp=0 --skip_trx=true  --report-interval=1 --mysql-ignore-errors=all 
--mysql_storage_engine=innodb --auto_inc=off --histogram  --stats_format=csv --db-ps-mode=disable --point-selects=50 
--reconnect=10 --range-selects=true –rate=100 --threads=<#Threads from 2 to 4096> --time=1200 run

The article then describes:

• The environment
• The tests
• Conclusions

HAProxy comes up as the champion in this test; there is no doubt that it could scale the increasing load in connection without being affected significantly by the load generated by the requests. The lower consumption in resources also indicates the possible space for even more scaling. For further details, charts and tests results follow the link to the original article. Interesting!