Streaming Architectures

This is the element that takes data from a source, called a producer, translates it into a standard message format, and streams it on an ongoing basis. Other components can then listen in and consume the messages passed on by the broker.

Kafka Monitoring

This is the element that takes data from a source, called a producer, translates it into a standard message format, and streams it on an ongoing basis. Other components can then listen in and consume the messages passed on by the broker.

Streaming brokers support very high performance with persistence, have a massive capacity of a gigabyte per second or more of message traffic, and are tightly focused on streaming with little support for data transformations or task scheduling (although Confluent's KSQL offers the ability to perform basic ETL in real-time while storing data in Kafka).

Kafka Producer

The Kafka producer is conceptually much simpler than the consumer since it does not need group coordination. A producer partitioner maps each message to a topic partition, and the producer sends a produce request to the leader of that partition.

Kafka Consumer

This section gives a high-level overview of how the consumer works and an introduction to the configuration settings for tuning.

Batch & Real Time ETL

Some stream processors, including Spark, provide a SQL syntax for querying and manipulating the data; however, for most operations, you would need to write complex code in Scala. Upsolver's data lake ETL is built to provide a self-service solution for transforming streaming data using only SQL and a visual interface, without the complexity of orchestrating and managing ETL jobs in Spark.

Use Case

Modern data streaming platforms automatically process information, integrate data from numerous sources, and help organize, manage, and act on this data continues to grow in volume and complexity.

Spark Deployment

Deployment Spark using Helm chart from Bitnami Repository. Our application containers are designed to work well together, it's extensively documented, and like our other application formats, our containers are continuously updated when new versions are made available.

Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object in your main program (called the driver program)

Kafka Monitoring

Monitor Kafka Cluster healthiness using Prometheus & Grafana. To monitor Kafka system health in predefined dashboards and to alert on triggers. Additionally, Control Center reports end-to-end stream monitoring to assure that every message is delivered from producer to consumer, measures how long messages take to be delivered and determines the source of any issues in your cluster.

Istio Loadbalancer

Istio's traffic routing rules let you easily control the flow of traffic and API calls between services. Istio simplifies the configuration of service-level properties like circuit breakers, timeouts, and retries, and makes it easy to set up important tasks like A/B testing, canary rollouts, and staged rollouts with percentage-based traffic splits.

Druid Deployment

Apache Druid is a real-time analytics database designed for fast slice-and-dice analytics (“OLAP” queries) on large data sets. Druid is most often used as a database for powering use cases where real-time ingest, fast query performance, and high uptime are important.

Atlas Deployment

Atlas requires TLS for cluster connectivity and does not surface options for disabling TLS. Atlas suits users who want fewer moving parts to manage, enabling developers and database administrators to be more productive.