by Steven Xi, Siyu Jia, Cindy Le

Processing and storing massive data in real time is hard, especially with the need to restore data integrity and velocity. Building reliable and efficient data streaming pipelines has become mission-critical and a bottleneck for many organizations.

Since Kafka is a popular open-source data streaming community, some may ask, why invest in another data streaming and messaging company? Our thesis is simple: we believe the market is huge, multiple players can co-exist, and that StreamNative, a next-generation unified data streaming and messaging platform based on Apache Pulsar, brings unique and powerful capabilities to the market.

The massive and growing market is not winner-take-all. We believe that as companies’ messaging needs continue to evolve in data streaming, message queuing, and the need for unified messaging and streaming, StreamNative will continue to gain market share.

As many developers know, Confluent, which is based on Apache Kafka, a project that originally began in LinkedIn in 2011 and was committed to open source in 2014, pioneered data streaming and messaging, but was not built natively for the cloud. StreamNative, by comparison, was built for Kubernetes and cloud-native from inception. Due to its cloud-native architecture, Pulsar is more horizontally scalable than Kafka, with fewer laborious tasks like sharding or adding machines required.

As a unified messaging and streaming platform powered by Apache Pulsar, StreamNative has several advantages over its competition. For example, it has low latency in data processing. According to Nastel, a middleware market intelligence company, at higher message rates, Pulsar achieved the highest benchmarking test score among 7 products they tested. As one of the customers put it, “Pulsar vs. Kafka is like Spark vs MapReduce, huge differences. Especially when coming to message queuing and log streaming, Pulsar provides cutting-edge experience.”

StreamNative’s solutions are cost-efficient. Decoupling storage from computing has primarily reduced the demand for preserved capacity. In this way, organizations can afford to store event streams for longer durations. It further reduces the cost by assigning the data to suitable tiers based on its length of history and business value.

In addition to its technical advantages, StreamNative also has deep roots in the open-source community. Sijie Guo, the CEO, and Matteo Merli, the CTO, are among the top three committers of Apache Pulsar. Pulsar has 560+ contributors and 11.3k+ stars on GitHub as of this post, roughly half of those of Kafka, but its number of monthly active contributors now surpasses that of Kafka. The rapid adoption of Apache Pulsar and its growing open source community have brought huge benefits to StreamNative in the form of organic customer growth, with most customers coming directly from the Apache Pulsar community.

Because of its scalable infrastructure and cloud-native approach, StreamNative achieved 6x growth in revenue in 2021 and continues to see strong growth in 2022. Top markets include fintech, martech, consumer, retail, manufacturing, and IoT, and expanding partnerships with major cloud vendors are unlocking new channel opportunities.

We at Eastlink are thrilled to have led StreamNative’s last closing of its $23.7M Series A financing round in 2021. We share StreamNative’s vision of connecting every app, anywhere in the world, and are committed to helping founders to succeed with strategic, operational, and technical expertise.

*The content is provided for informational purposes only, and should not be viewed as legal, business, investment, or tax advice.

In a recently published benchmark report, TigerGraph’s powerful graph analytics software was put to the test using the respected Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) Scale Factor 30k dataset, which features 36TB of raw data with 73 billion vertices and 534 billion edges. This was, as far as we can ascertain, the first time a graph database has been tested at this scale.

The LDBC SNB benchmark is an industry-respected testing methodology for confirming a graph platform’s performance while executing complex business intelligence and advanced analytics tasks.

This new study clearly demonstrates TigerGraph’s ability to handle a big graph workload in a real production environment, where tens of terabytes of connected data with hourly or daily incremental updates is the norm.