Apache Nifi

Currently, at multiple end-point systems, you can find data gathering and here are a few click streams to name like legacy systems, sensors, weblogs, clickstream. There is a new challenge in the process of gathering data and maintaining them. Data flows are scheduled relying on a set of input conditions, for instance, Apache Falcon permits you to configure and schedule data.

For gathering data from various end systems and aggregating them, the flume is a very good fit. As a combination address while this system independently talks a lot about the key requirements for gathering data at a real-time shows few significant aspects of data flow and data collection.

There are different delays in data coming from the similar systems and sensor which are not the same. This is termed as gathering data at the jagged edge or ragged edge. Moreover, such systems are at various geographic locations with latency and network bandwidth.

For about 8 years if the platform Nifi was around before it was unveiled by open sourced and incubated ASF.

  • Guaranteed Delivery

Even during very high scale, a core philosophy of NiFi has been having a guaranteed delivery. With the effective use of purpose-built persistence, this can be achieved by write-ahead log and content repository. For very high transaction rates, they are designed to permit it when they are combined together. At least once semantics of data delivery is permitted by Nifi.

  • Data Buffering/Back Pressure and Pressure Release

All the queued data that is buffered is assisted by NiFi along with the ability to offer back pressure as those queues touch limits which are specified for aging off data as it reaches a specified age.

  • Prioritized Queuing

The setting of more than one prioritized schemes is permitted by NiFi for how to retrieve the data from a queue. There is the oldest default key first but there are times when the data must be newly pulled with largest first or few another custom scheme.

  • Flow Specific QoS

In the place where data is completely important and does not tolerate the loss. You can find few times about its process and its delivery within seconds of any value.

The fine-grained is enabled by NiFi by flowing particular configurations of these concerns.

  • Data Provenance

There are indexes, records that are made automatically by NiFi in making the existing provenance data as objects flow via the system. In assisting the compliance this information becomes quite important which is also troubleshooting with optimization and other scenarios.

  • Visual Command and Control

A visual establishment is enabled by NiFi with data flows in real-time. It offers a UI based approach for building design flows. Apart from that, it permits you to include or delete data flows in a deployed flow.

  • Flow Templates

There is a high pattern orientation followed in data flows and there are lots of various ways to solve a problem and it assists greatly in sharing those best practices. Subject matters are permitted by templates for building and publishing their flow designs for various benefit and collaboration on them.

  • Clustering

For scaling out the cluster usage, NiFI is designed with lots of nodes combined as mentioned above. The most effective thing is the usage of NiFi’s site to site feature as it permits the NiFi and a client for communicating with each other for exchanging data on particular authorized ports.

  • Security

A data flow with system to system is not only good till it is secured and NiFi at all points in a data flow provides secure exchange via the use of protocols with encryption like 2-way SSL. Apart from that NiFi permits the encryption and decryption content flow with the shared-keys utilizations or other mechanisms on a various side of the recipient or sender equation.

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Reference site: thedatateam

Author name: Kaushik Chatterjee