Flume is a data ingestion tool in the Hadoop world. Flume basically collects, aggregates and moves large amounts of streaming data into centralized data stores such as HDFS. It is primarily used for log aggregation from various sources and then finally pushed to HDFS. I give you one real-world example: suppose amazon wants to analyse customer behaviour from a particular region. It has a huge amount of log database assignment help which is getting generated from the activity of users on amazon website. So, this log or even data getting generated needs to be ingested into HDFS and to capture this type of data that is generating in real-time flume is an appropriate tool. Flume is basically ingesting streaming data into HDFS means designed to capture data like real-time or streaming data then channel it to HDFS for storage and subsequent processing. Each data item captured is considered an event so Flume collects the data or events and aggregates them to put them in HDFS.
Flume vs Sqoop
Flume and sqoop are both data ingestion tools. Flume is used to ingest streaming data while sqoop is used to ingest data from any kind of relational databases like Oracle or my sql etc. Flume is used for collection and aggregation of data typically of log data while sqoop transfers data parallelly by making a connection to the database for HADOOP assignment help from experts. Both tools are quite popular in real-world scenarios, for example goibibo uses flume to transfer log data into HDFS while coupons.com uses sqoop to transfer data between its IBM Netezza database and Hadoop word.
Events are generated by external sources like web servers and are consumed by flume data source so what is an event. Flume represents data as events for example each log entry saved in a web server can be considered as an event. For a new post added on Twitter can also be considered as an event now these events for database assignment help together are consumed by flume. Data sources the external source sends events to flume in a format that is recognized by the target source. Flume agent is an independent daemon process. It is a kind of jvm or we can also say in simplest way that it is a simplest unit of flume deployment. Each flume agent has three components: the source, channel and the sink. Flume source receives an event and stores it into one or more channels the channel acts as a go down on a storehouse which keeps the events until they are consumed by the flumes. The flume sink removes the events from channels and stores it into an external repository for example HDFS or to add another flume agent so there can be more than one flume agent in which flume sink forwards the events to the flume source of the other flume agent in the data flow.
Building blocks of flume
- Source is responsible to send the event to the channel it is connected to.
- It may have logic relating to reading data, translating to events or handling failures.
- It has no control over how the event is stored in the channel.
- There can be many flume sources of data that flume supports like netcat, exec, lavro, and sequence file generator, TCP, UDP, thrift and protocol buffers as source of data.
- Channel connects the source or sources and the sink or sinks.
- Channel acts as a buffer with configurable capacity.
- Channel can be either in a memory or a database. so a durable channel is a must for recoverability.
- Weights for events from the configured channel.
- It is responsible to send the event to the desired output.
- It manages issues like timeouts or retries.
- It can set up sync groups like a group of priority sinks to manage sync failures and as long as one sink in the group is available the agent will function.
- Spark Core – this is the base engine and this is used for large-scale parallel and distributed data processing. It has rdd’s as the building blocks of your spark so it is responsible for your memory management in big data assignment help analytics from top database experts, your fault recovery, scheduling, distributing and monitoring jobs on a cluster and interacting with storage systems so here I would like to make a key point that spark by itself does not have its own storage. It relies on storage now that storage could be hdfs, it could be a database like NoSQL database such as HBase or it could be any other database say our DBMS from where you could connect your spark and then fetch the data, extract the data, process it and analyse it.
- RDD – As the name says it is resilient so it is existing for a shorter period of time distributed so it is distributed across nodes and it is a data set where the data will be loaded or where the data will be existing for processing so it is immutable, fault tolerant. There are mainly two operations Transformation and Action, which can be performed on an RDD.
- Spark SQL- spark SQL is a component a processing framework which is used for structured and
- Semi-structured data. TechyFleX Spark SQL has something called a data frame API. Data frames in short you can visualize or imagine as rows and columns or if your data can be represented in the form of rows and columns with some column headings so data frame API allows you to create data frames.