How HubSpot's analytics engineering team gained pipeline autonomy
Learn how Oviya Arasu and her team at HubSpot use Fivetran data models and Transformations for dbt Core™ to automate their pipelines and decrease engineering bottlenecks.
Brief overview of Cloudera Stream Processing
Cloudera Stream Processing lets developers and analysts build real-time data products using industry-standard SQL. In this demo, you will see SQL Stream Builder quickly transform Kafka topics into tables to be queried and SQL queries transformed into continuous Flink jobs.
5 Ways to Use Log Analytics and Telemetry Data for Fraud Prevention
As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.
Unleashing the power of Fivetran and Azure Synapse for accelerated data movement
Say goodbye to constant “One-Offs” and hello to “Set It and Forget It”
Five real-world examples of database analytics
Our customers share why they chose Fivetran to replicate their database data and how their analytics have improved as a result.
Deploying Machine Learning Models for Real-Time Predictions Checklist
Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies. In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process.
Is Your Data Speaking to You? Real-Time Anomaly Detection Helps You Listen Effectively
As we hurtle into a more connected and data-centric future, monitoring the health of our data pipelines and systems is becoming increasingly harder. These days we are managing more data and systems than ever before, and we are monitoring them at a higher scale.
[ MLOPS ] Experiment management and remote execution using YOLOv8 and ClearML
Experiment management and remote execution using YOLOv8 and ClearML.
Future of Data Integration: ROI of No-Code Platforms
The future of data integration is no-code data integration platforms, and we have the research to prove it.
Running Ray in Cloudera Machine Learning to Power Compute-Hungry LLMs
Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.