Your 12 hourly digest for DZone.com Feed

DZone.com Feed Recent posts on DZone.com

Rolling Numbers Animation Using Only CALayers Apr 11th 2023, 14:36, by Max Kalik I'm an iOS Engineer at Triumph Labs, where I develop TriumphSDK for game devs. Not so long ago, I got a task to completely reimplement UI and animations. One of the most interesting parts was implementing of balance view with rolling animation of the number in it. At the first sight, it looks pretty simple, but we needed a well-configured custom solution. Of course, I checked existing libraries that I could reuse and adjust this kind of animation for our needs. After the first try, I figured out that the libraries are not so adjustable, and we cannot control the animation of each number. I won't go into details too much. However, I decided to make my own solution from scratch. Lessons Learned From Picking a Java Driver for Amazon ElastiCache for Redis - Part 2 Apr 11th 2023, 14:27, by Jeroen Reijn In part one of this series, we looked at Jedis as the Java-based Redis driver when used in combination with ElastiCache for Redis in clustered mode. We learned that during a failover, Jedis takes some time to figure out what the new primary is before it can restore the connection to the ElastiCache cluster. In this post, I'll share some of the lessons learned from switching to Lettuce as the driver for our Java-based application. A Different Driver Lettuce, a Java-based driver for Redis, is the default driver for Redis when working with Spring Framework. Lettuce is a fully non-blocking Redis client built with Netty providing Reactive, Asynchronous, and Synchronous Data Access. What Are Microservices Architecture and How Do They Work? Apr 11th 2023, 13:54, by Daryus Aden In the era of mobile computing, app developers should be able to deploy actions quickly and make changes without redeploying the entire app. As a result, it has led to a new way of building software called "microservices." Microservices are small, self-contained parts of an app that each do their job and talk to each other through APIs.  Even though these microservices are different, they work together to complete the job. Microservices are becoming an essential part of modern app architecture, so let's talk about what microservices architecture is, how they work, and why they are helpful. Handling Bad Messages via DLQ by Configuring JDBC Kafka Sink Connector Apr 11th 2023, 13:53, by Gautam Goswami Any trustworthy data streaming pipeline needs to be able to identify and handle faults. Exceptionally while IoT devices ingest endlessly critical data/events into permanent persistence storage like RDBMS for future analysis via multi-node Apache Kafka cluster. (Please click here to read how to set up a multi-node Apache Kafka Cluster). There could be scenarios where IoT devices might send fault/bad events due to various reasons at the source points, and henceforth appropriate actions can be executed to correct it further. The Apache  Kafka architecture does not include any filtering and error handling mechanism within the broker so that maximum performance/scale can be yielded.  Instead, it is included in Kafka Connect, which is an integration framework of Apache Kafka. As a default behavior, if a problem arises as a result of consuming an invalid message, the Kafka Connect task terminates, and the same applies to JDBC Sink Connector. Kafka Connect has been classified into two categories, namely Source (to ingest data from various data generation sources and transport to the topic) and Sink (to consume data/messages from the topic and send them eventually to various destinations).  Without implementing a strict filtering mechanism or exception handling, we can ingest/publishes messages inclusive of wrong formatted to the Kafka topic because the Kafka topic accepts all messages or records as byte arrays in key-value pairs. But by default, the Kafka Connect task stops if an error occurs because of consuming an invalid message, and on top of that JDBC sink connector additionally won't work if there is an ambiguity in the message schema. Private DNS Zone With Azure HDInsight Kafka Apr 11th 2023, 13:26, by Prakashkumar Patel What Is HDInsight Kafka? Azure HDInsight Kafka is a cloud-based, managed Apache Kafka service offered by Microsoft Azure. Apache Kafka is an open-source, distributed streaming platform that allows for the processing and analyzing of high-volume, real-time data streams. Azure HDInsight Kafka provides a fully managed and scalable environment for deploying and running Apache Kafka clusters, allowing users to easily create, configure, and manage Kafka topics, producers, and consumers. It also provides integration with other Azure services, such as Azure Data Lake Storage, Azure Event Hubs, and Azure Stream Analytics, enabling users to ingest, process, and analyze large volumes of data in real-time. Streaming in Mule 4: Processing Large Data Sets Apr 11th 2023, 13:07, by Ishalveer Singh Randhawa Mule is a lightweight enterprise service bus and integration framework provided by MuleSoft. The platform is Java-based and hence makes use of the JVM for process execution. It is the fundamental task in MuleSoft to integrate different systems, and there are scenarios where we take data from one system, then process it, and finally load it into another system (ETL), where these source and end systems can be Database, Salesforce, SFTP/FTP or Files. There can be various approaches we can adopt to achieve the above goal. But when processing a reasonable amount of data, one of the concerns designers and developers need to address is the potential of retrieving an enormous number of results in a single load session because if the size of the data being loaded into the JVM Heap Memory exceeds its size, we get the memory out of bound exception, our application crashes, and the process execution fails. Some Conventional Solutions To address the problem we encountered in the above scenario, we can make use of the following logic: Data Modernization Stages and Best Practices Apr 11th 2023, 12:23, by Tejas Kaneriya Data is a priceless resource for any company. But, the pace of innovation using data differs from organization to organization. Organizations with sprawling legacy systems slow down innovation, while cloud-native ones rapidly benefit from modern analytics and AI through more straightforward implementation. Companies with sizeable on-site data environments may plan to upgrade their data and use AI to overcome business obstacles. However, the transition from on-site to AI may seem intimidating to them. Team Mentoring and Learning Path Apr 11th 2023, 11:39, by Lokesh Raj Considering a lot of demand for the various skill sets of people for upgrading existing systems or for new projects, it is very challenging to prepare the best team for the project.  A few successful processes could improve the situation; I have listed some of them below and hope they are helpful.

You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our policies, terms and conditions. If you no longer wish to receive these emails, you can unsubscribe from this feed, or manage all your subscriptions.