Successful data-driven companies like Uber, Facebook, and Amazon rely on real-time analytics. Personalizing customer experiences for e-commerce, managing fleets and supply chains, and automating internal operations require instant insights into the freshest data.
To deliver real-time analytics, companies need a modern technology infrastructure that includes three things:
There are numerous benefits of having a unified advertising mechanism for the business of an organisation. Today, most of the advertising that is done across various media is disparate and not having a unified mechanism for generation, distribution, maintenance, reporting, or collection. To bridge this gap, I conceptualized and coined the term Meld Advertising (Referred to as ‘It’). Meld Advertising brings out all of these in the form of providing tools to automate most of these processes and to directly connect with all unique advertising channels. The tools help in the creation, beta, subscription, payments, real-time reporting, cost-effectiveness, and overall maintenance of the advertisements.
It helps all types of users including the end contractors who work on actually putting up advertising for diverse media. These include all tools which facilitate the entire workflow of advertising. It also recognizes that usage of effective tools, which are simple to use and create advertisements online, will lead to a faster execution of marketing campaigns. Also, ready-made templates, tie-ups with other advertising tools, and online expert help will allow for a more efficient generation of advertisements.
I am excited to share my experience with Spark Streaming, a tool which I am playing with on my own. Before we get started, let’s have a sneak peak at the code that lets you watch some data stream through a sample application.
from operator import add, sub
from time import sleep
from pyspark import SparkContext
from pyspark.streaming import StreamingContext # Set up the Spark context and the streaming context
sc = SparkContext(appName="PysparkNotebook")
ssc = StreamingContext(sc, 1) # Input data
rddQueue = []
for i in range(5): rddQueue += [ssc.sparkContext.parallelize([i, i+1])] inputStream = ssc.queueStream(rddQueue) inputStream.map(lambda x: "Input: " + str(x)).pprint()
inputStream.reduce(add) .map(lambda x: "Output: " + str(x)) .pprint() ssc.start()
sleep(5)
ssc.stop(stopSparkContext=True, stopGraceFully=True)Spark Streaming has a different view of data than Spark. In non-streaming Spark, all data is put into a Resilient Distributed Dataset, or RDD. That isn’t good enough for streaming. In Spark Streaming, the main noun is DStream — Discretized Stream. Thats basically the sequence of RDDs. The verbs are pretty much the same thing — the way we have actions and transformations with RDDs, we also have actions and transformations with DStreams.
