Spark is an unified engine designed for large scale distributed data processing and machine learning on compute clusters, whether running on-premise or cloud. It replaces Hadoop MapReduce with its in-memory storage for intermediate computations, making it much faster (100x) than Hadoop MapReduce.
The Kerberos is an authentication protocol which creates tickets to allow communication between nodes on non-secured network. Ticket must be periodically triggered by kinit command by each user. In Kerberos we call users as principals. We can divided principals basically into several groups:
YARN stands for Yet Another Resource Negotiator. It was introduced in Hadoop version 2 to extend other data processing framework to not only Map Reduce such as Spark, Storm, etc.
Apache Kafka is a distributed streaming platform. It is used for building real-time data pipelines and streaming apps. It is horizontally scalable, fault tolerant, wicked fast, and runs in production in thousands of companies.
Along with the widely cloud adoption, integrated development environment (IDE) on browser is a need to boot developers’ productivity. People can collaboratively view, edit and commit on any devices with internet accessed browser. Additionally, you’re no longer worry about setting up your local development config. You can consider Cloud9 (AWS) or paid service like codeanywhere.
Kibana is part of ELK stack to visualize data from elasticsearch. Further than that, Kibana is equipped with many features and plug-ins such as elastic nodes & infrastructure monitoring, user roles or life cycle management and query experiment elasticsearch database.
Spend sometime with the demo Kibana page to feel it. Click Here.
Grafana is an open-source web application specialized in time-series visualization. Hence, it is suitable for the the purpose of monitoring. One of interesting facts is that Grafana is a fork from Kibana 3 to enhance the ability of dashboard editing and make it as a clean and elegant time-series visualization tool. To get the sense of Grafana dashboard, surf this link: https://play.grafana.org.
When do researching to choose a good data storage technique for log collection, searching and analytic; I found elasticsearch is a ideal choice because of following reasons:
Default Apache Nifi installation comes without security layer which exposes the development UI. As a result, users can freely access the Nifi project development with knowledge about the hostname and binding Port. You can see two potential security risks:
The goal of this project is to collect and visualize the stock price of all tickers in Vietnam. There is quite limited access to API for a single business user, this project aim at scrap data from website, clean, extract and load into data warehouse. The final product is a maintainable/reliable data pipeline with exposed analytic dashboard hosted on cloud, and end authorized users can access to 24/7 with daily updated data.
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