Systems | Development | Analytics | API | Testing

How Does Tier 2 SOC Automation Work?

Tier 2 SOC work picks up evidence gathering across consoles, containment decisions, sandbox detonation and verdicting, sweeping new indicators through historical data, and the case documentation and handoff that follow. Tier 1 work is linear enough to enumerate, so a playbook can list the steps. Tier 2 investigations branch, since each answer changes the next question, and no engineer can pre-write every path and that is why SOAR does not do well in Tier 2 even in teams where it works well at tier 1.

Demo: Flink Table API, User-Defined Functions (UDFs), Process Table Functions (PTFs)

Fully managed Confluent Cloud for Apache Flink combines Flink SQL for data engineers with programmatic capabilities like the Table API, User-Defined Functions (UDFs), and Process Table Functions (PTFs) for developers. You can build mission-critical use cases with programmatic capabilities while using familiar dbt- and SQL-based workflows.

Demo: Real-Time Forecasting with IBM Granite Time Series Models, Apache Flink, and Apache Kafka

Perform robust real-time forecasting on your Kafka data streams using IBM Granite Time Series models TTM, FlowState, PatchTST-FM right in Flink. Get started easily with zero-config, model flexibility, and lower cost in fully managed Confluent Cloud.

Demo: Real-time anomaly detection and forecasting, Real-Time Context Engine for AI agents, and more

What's new in Q3'26: Real-time anomaly detection and forecasting with IBM Granite Time Series models and Google TimesFM model, upserts for Real-Time Context Engine to maintain fresh context for AI, and lightning queries to instantly serve the current state of the business for operational apps, analytics, and more.

Build Custom, AI-Ready API Endpoints Without Writing Backend Code

Auto-generated APIs changed how fast teams ship. Point DreamFactory at a database and you get a complete REST API in seconds: every table, full CRUD, live documentation, role-based security. For thousands of teams, that is the whole job. But auto-generated APIs mirror your schema. Your applications, and increasingly your AI agents, want something more deliberate: clean paths, shaped responses, and endpoints that match how the consumer thinks rather than how the database is laid out.

5 Ways Automation Is Improving Food Manufacturing Quality and Safety

Food manufacturers have always operated under intense pressure to deliver products that are both consistent and safe. A single contamination event or a batch of mislabeled allergens can trigger recalls, damage brand trust, and put consumers at risk. As production volumes grow and supply chains stretch across borders, manual inspection processes are struggling to keep pace. That's where automation is stepping in, transforming how food is monitored, tested, and cleared for shelves. Continue reading to learn more about how automation improves food manufacturing.

A language server for your bitrise.yml: autocomplete, validation, and navigation

The Configuration YAML view in the Bitrise Workflow Editor now reads your entire bitrise.yml, and tells you while you type whether it's correct for your project. That means real-time validation, autocomplete, hover docs, and go-to-definition/references for anyone who edits bitrise.yml by hand. No install, no setup, available on every plan down to Hobby.

Who's Responsible When AI Gives You the Wrong Answer? Andy Cotgreave and Francois Lopitaux Debate

If an AI agent gives a business user the wrong number, and a consequential decision gets made off it, who's responsible? The data analyst who built the semantic layer? The platform? Or the business user who asked the question and acted on the answer?