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Streaming Audio: Apache Kafka® & Real-Time Data

Streaming Audio: Apache Kafka® & Real-Time Data

Technology

Powering Real-Time Analytics with Apache Kafka and Rockset

Powering Real-Time Analytics with Apache Kafka and Rockset

2021-07-15
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Using large amounts of streaming data increasingly requires interactive, real-time analytics and dashboards—and this applies to any industry, including tech. CTO and Co-Founder of Rockset Dhruba Borthakur shares how his company uses Apache Kafka® to perform complex joins, search, and aggregations on streaming data with low latencies. The Kafka database integrations allow his team to make a cloud-native analytics database that is a fundamental piece of enterprise infrastructure. 

Especially in e-commerce, logistics and manufacturing apps are typically receiving over 20 million events a day. As those events roll in, it is even more critical for real-time indexing to be queried with low latencies. This way, you can build high-performing and scalable dashboards that allow your organization to use clickstream and behavioral data to inform decisions and responses to consumer behavior. Typically, the data follow these steps:

  1. Events come in from mobile or web apps, such as clickstream or IoT data
  2. The app data is sent to the cloud
  3. Data is fed into the database in real time
  4. This information is shared live on a dashboard or via SaaS application embeds

For example, when working with real-time analytics in real-time databases, both need to be continuously synced for optimal performance. If the latency is too significant, there can be a missed opportunity to interact with customers on their platform. You may want to write queries that join streaming data across transactional data or historical data lakes, even for complex analytics. You always want to make sure that the database performs at a speed and scale appropriate for customers to have a seamless experience. 

Using Rockset, you can write ANSI SQL on semi-structured and schemaless data. This way, you can achieve those complex joins with low latencies. Further data is required to supplement streaming data, but it can be easily supported through supported integrations. By having a solution for database requirements that are easily integrated and provide the correct data, you can make better decisions and maximize the result. 

EPISODE LINKS

  • Real-Time Analytics and Monitoring Dashboards with Apache Kafka and Rockset
  • Watch the video version of this podcast
  • Join the Confluent Community
  • Learn more with Kafka tutorials, resources, and guides at Confluent Developer
  • Live demo: Kafka streaming in 10 minutes on Confluent Cloud
  • Use 60PDCAST to get an additional $60 of free Confluent Cloud usage (details)
view more

More Episodes

Apache Kafka 3.5 - Kafka Core, Connect, Streams, & Client Updates
2023-06-15
A Special Announcement from Streaming Audio
2023-04-13
How to use Data Contracts for Long-Term Schema Management
2023-03-21
How to use Python with Apache Kafka
2023-03-14
Next-Gen Data Modeling, Integrity, and Governance with YODA
2023-03-07
Migrate Your Kafka Cluster with Minimal Downtime
2023-03-01
Real-Time Data Transformation and Analytics with dbt Labs
2023-02-22
What is the Future of Streaming Data?
2023-02-15
What can Apache Kafka Developers learn from Online Gaming?
2023-02-08
Apache Kafka 3.4 - New Features & Improvements
2023-02-07
How to use OpenTelemetry to Trace and Monitor Apache Kafka Systems
2023-02-01
What is Data Democratization and Why is it Important?
2023-01-26
Git for Data: Managing Data like Code with lakeFS
2023-01-19
Using Kafka-Leader-Election to Improve Scalability and Performance
2023-01-12
Real-Time Machine Learning and Smarter AI with Data Streaming
2023-01-05
The Present and Future of Stream Processing
2022-12-28
Top 6 Worst Apache Kafka JIRA Bugs
2022-12-21
Learn How Stream-Processing Works The Simplest Way Possible
2022-12-20
Building and Designing Events and Event Streams with Apache Kafka
2022-12-15
Rethinking Apache Kafka Security and Account Management
2022-12-08
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