Amazon QuickSight The following diagram explains how the services work together: None of the included services require the creation, configuration, or installation of servers, clusters, and databases. In this example, you use these services to send and process simulated streaming data of sensor devices to Kinesis Firehose and store the raw data in S3. Using AWS Glue, you analyze the raw data from S3 in batch-oriented fashion to look at the thermostat efficiency over time against the historical data, and store results back in S3.
What we had was a strategy function, which supported marketing and other organisational priorities. One thing led to another, and I felt this was an area that interested me. Big Data is a big mystery for the uninformed. How would you demystify it for those who are not familiar with data and analytics?
Today, data is witnessing an astronomical growth through the digital revolution, numerous systems, machines, servers and the Internet of Things. As a result of consumerism and the ubiquity of connected smart devices and socially connected platforms, corporates now have the opportunity to understand their customers better by leveraging the data generated at various touchpoints.
Every single consumer interaction leads to the generation of varied types of data, for instance, audio or voice data when dialling a customer service centre or a call centre, text data when writing a review on TripAdvisor, transaction data when making an online purchase, and so forth.
Consequently, the data being generated varies considerably in size, shape and form across marketing and communication channels and other customer touchpoints, and hence the term Big Data — because it is voluminous, big and of many types. This data is also generated at different speeds — some in a second, others in a week, or in months.
Today, every device in our daily lives and every equipment in a factory comes with smart sensors that provide us valuable information by the minute in fact, every millisecond.
Mining such high-velocity and high-volume data can transform businesses and open up newer ways of impacting our lives.
Big Data analytics helps you make sense of this data. It gives you the ability to store, process and mine this massive volume of data really fast. Organisations make better and smarter decisions through the insights and predictions generated from mining this data.
And, regardless of the business and function that you are in, it is a strong ammunition and aid to making judicious decisions and enhancing profits. Is India ready for the Big Data play or are we still at a nascent stage?
India, today, is one of the largest talent bases when it comes to data analytics, with overpeople involved with it in some way. However, a majority of them service foreign companies, which look at India for its economies of scale.
So, as big as the analytics talent base is, the irony is that a lot of it is not catering to Indian businesses.
This is because of a number of reasons, especially the state of the data ecosystem here. Data continues to be a challenge in India, in terms of quality, coverage, consistency, as well as its understanding and awareness.
India clearly has a while to go in terms of the adoption of analytics compared with certain developed markets; but I think we are at the cusp of a transformation. Two or three years from now, as organisations compete on a global scale, Big Data will be a non-negotiable competency for every organisation — they will just not be in a position to compete without the power of data analytics.
The government has done wonders in terms of the digital revolution and in democratising data; we have the largest millennial population; and we have million internet subscribers and 1 billion cellphone subscribers, including million smartphone users.
What all of this means is that there is a nexus of forces connecting customers, and they can help organisations gather and mine data and use it for business benefit. We are observing that, in the context of industries such as e-commerce and smart devices, the gap between the so-called advanced and emerging markets is almost non-existent today.
It is my strong belief that other industries and business functions will not be left far behind. How did the idea of Tata iQ come about? As the Tata group looked at various strategic initiatives, one thing that came out loud and clear was the tremendous asset that the group has in terms of consumer connect, both B2B and B2C, across companies within and outside India.
Tata iQ was conceptualised to do two things. One was to help Tata companies adopt analytics for smarter decision making across various functions.
The other was to create one of the richest and most valuable databases on Indian consumers and institutions with holistic intelligence and attributes pertaining to their demographics, attitudes, behaviour, spending habits, passions, etc.
This will generate actionable insights and more precise action to help every company gain higher market share. During the last 18 months, we have built an entire Big Data ecosystem that now allows us to store, process and mine a massive variety of information in real time, in batches.
We have assembled a great team of data scientists drawn from diverse sectors, global firms and top institutes. We have also successfully partnered with over 15 Tata companies in proof-of-concepts and pilots to solve a wide spectrum of business problems across sales and marketing, operations, risk, collections, digital, finance and HR functions.
Here we deployed advanced analytics solutions such as geospatial, social media and web analytics, survival analysis, machine learning, natural language processing, stochastic gradient boosting and neural network.
Very recently, our team has been entrusted with launching a loyalty programme for the Tata group that aims to recognise a lifetime of relationships with consumers through exclusive benefits and privileges, and provide a key enabler to customer centricity within the Tata group.Choose Send data.
Without leaving the KDG page, navigate to the Kinesis Analytics console to view the status of the application processing the data in real time.
When you are happy with the amount of data sent, choose Stop Sending Data to Kinesis.I recommend waiting until at least 20, records are sent. How does in-memory processing of data for real-time streaming analytics, versus first writing to storage, change the way we need to think about applications?
Steve Wilkes Steve Wilkes: It turns out we are producing way too much data to store and analyze afterwards. Get deep learning analytics and insights live from streaming data.
Review logs from website clickstream in near real-time for advanced analytics processing. Spark Streaming supports real-time processing of fast moving, streaming data to gain insights for business and make business decisions in real-time or near real-time. It is an extension to Spark core to support stream processing.
Spark Streaming is production-ready and is used in many organizations. The kdb+ time-series database and the q programming language, are used for real time,streaming data analytics, in-memory computing and historical data analysis.
Hadoop is a widely used tool for historical big data analytics, but it is not designed to handle streaming, real-time data. Better options include Spark Streaming, Storm, Apache Flink, or Apache Samza.