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Imply Closes $70 Million Series C at $700M Valuation to Extend Leadership as Foundational Platform for Analytics-in-Motion

Imply Closes $70 Million Series C at $700M Valuation to Extend Leadership as Foundational Platform for Analytics-in-Motion
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Advertiser ie - Druid, GIAF, and Baboró in line for Business to Arts Awards

Launch HN: SigNoz (YC W21) – Open-source alternative to DataDog

Hi HN, Pranay and Ankit here. We’re founders of SigNoz ( https://signoz.io ), an open source observability platform. We are building an open-core alternative to DataDog for companies that are security and privacy conscious, and are concerned about huge bills they need to pay to SaaS observability vendors. Observability means being able to monitor your application components - from mobile and web front-ends to infrastructure, and being able to ask questions about their states. Things like latency, error rates, RPS, etc. Better observability helps developers find the cause of issues in their deployed software and solve them quickly. Ankit was leading an engineering team, where we became aware of the importance of observability in a microservices system where each service depended on the health of multiple other services. And we saw that this problem was getting more and more important, esp. in today’s world of distributed systems.

How To Become a Data Engineer

How To Become a Data Engineer 8 Jan. 2021, Data engineering, 73891 views, The demand for data engineers is growing rapidly. According to hired.com, the demand has increased by 45% in 2019. The median salary for Data Engineers in SF Bay Area is around $160k. So the question is: how to become a data engineer? What Data Engineering is Data engineering is closely related to data as you can see from its name. But if data analytics usually means extracting insights from existing data, data engineering means the process of building infrastructure to deliver, store and process the data. According to The AI Hierarchy of Needs, the data engineering process is located at the very bottom: Collect, Move & Store, Data Preparation. So if your organization wants to be data/AI-driven then they should hire/train data engineers.

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