Systems | Development | Analytics | API | Testing

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17 Automated Testing Insights from an Industry Expert

In today's fast-paced digital landscape, software development companies face the challenge of delivering high-quality applications quickly. One solution that has gained significant traction is automated software testing. Organizations can achieve repeatability, improve time-to-release, and enhance overall software quality by leveraging automated testing tools and methodologies.

What Are Digital Twins? Learn About Digital Twin Software

Imagine being able to make a digital replica of one of the largest stadiums in the world, capturing everything from ceiling height to square footage. Now, imagine you could integrate that replica with real-life, predictive data. Then, you could use it to measure important details like stadium capacity, climate control, security wait times, video and audio syncing capabilities, and more. In this example, digital twin software enables events to run more smoothly and helps staff foresee and plan upgrades.

12 Times Faster Query Planning With Iceberg Manifest Caching in Impala

Iceberg is an emerging open-table format designed for large analytic workloads. The Apache Iceberg project continues developing an implementation of Iceberg specification in the form of Java Library. Several compute engines such as Impala, Hive, Spark, and Trino have supported querying data in Iceberg table format by adopting this Java Library provided by the Apache Iceberg project.

Ship sooner to Google Play: Release Management now supports Android production releases

Ship Android apps faster than ever. Our new features allow you to effortlessly integrate with your Google Account, create production releases and release directly to the Google Play Store. Read more on this, and what's coming next.

The Peak AI Platform Lets Businesses Tap Into The Power Of Artificial Intelligence

How does Peak leverage the power of Snowflake to provide businesses with the ability to integrate artificial intelligence right into their data infrastructure? In this episode of “Powered by Snowflake” host Daniel Myers explores that question with Peak Solution Engineer Chris Billingham, who also provides a demo of how the Peak platform works.

The Future of Data Pipelines: Trends and Predictions

The global data integration market size grew from $12.03 billion in 2022 to $13.36 billion in 2023, making it evident that organizations are prioritizing efficient data integrations and emphasizing effective data pipeline management. Data pipelines play a pivotal role in driving business success by transforming raw datasets into valuable insights that fuel informed decision-making processes.

Gradle vs. Maven: Performance, Compatibility, Builds, & More

Gradle is one of several Java development tools featured in Stackify’s Comprehensive Java Developer’s Guide, but it’s not the only build automation tool to consider. Maven is an older and commonly used alternative, but which build system is best for your project? With other tools, such as Spring, allowing developers to choose between the two systems, coupled with an increasing number of integrations for both, the decision is largely up to you.

3 pillars for supporting realtime update infrastructure in transportation and logistics apps

Amazon was founded in 1994, went public in 1997, and reached a market cap of $1.5 trillion in 2020. As a result of Amazon’s successes and a long tail of rapidly modernizing ecommerce businesses, consumers and businesses alike have transformed their expectations around transportation and logistics. Consumers, for example, expect up-to-the-minute updates on package delivery, and businesses require, among other features, realtime asset and vehicle monitoring.

Demo: Real-time Data Pipelines for Databricks Lakehouse with Qlik

Discover how Qlik's Data Integration Platform automates and accelerates your data pipeline for Databricks. In this demo, you will see why Databricks choose Qlik as their “Integration Partner of the Year”, with our Change Data Capture (CDC) capabilities, real-time data ingestion, and powerful analytics features. Enhance your AI, machine learning, and data science initiatives with secure and compliant data access. Deploy in any cloud configuration and integrate with diverse data sources.