As we proceed through this age of massive information and great technologies, we are obtaining many more angles from which we can record data. As a result, organizations like Google, Amazon, Facebook, Netflix, and Twitter are finding more and more uses for the information gained from analyzing this data in order to further their interests. In today’s competitive markets, the utilization of data is a key factor in determining who wins and who loses, but as the power of the internet grows exponentially, so does the amount and practicality of data. This sets the stage for what many call ** “Big Data”**, a new concept that is already changing the status quo in analytics.
Let's first see the definition of Big Data
In layman language, Big Data is the data, which is big in terms of Volume, has a huge Variety, and generates at a rapid Velocity. Just like in the case of social media platforms or e-commerce giants, where every second, millions of users generate petabytes of data.
In technical language, analyst Doug Laney famously differentiates Big Data from normal data in 5 aspects: Volume, Velocity, Variety, Veracity, and Value (or the 5 V's of big data) i.e. it has high volume, generates at high velocity, involves a variety of data sets, has high veracity or "truthiness", and provides high value at the end of data processing.
# The Paradigm of Big Data
Big Data encompasses technologies and processes involving data that is too complex and large, for traditional relational databases, to easily process and interpret.
Let's take a small glimpse of 5 V's of big data:
The volume of data in big data usually stands on the scale of petabytes of data or more. As of today, about 50000 petabytes of data is generated per day by more than 5 billion internet users across the globe. Youtube users watch an average of 4.3 million videos every minute which in turn generates petabytes of data for the company.
Big Data doesn’t come in a peaceful stream — it comes in torrents! Every minute an average of 600 new users sign up on social media platforms via mobile devices, and laptops adding to data flow every minute at a tremendous rate.
Big Data encompasses both structured and unstructured data. Before the world went digital, data was neatly organized and structured and was thus it was easy for data scientists to do data management using traditional data warehouses and relational databases. But today internet users produce tons of unstructured data like photos and videos on social media platforms, purchases on e-commerce platforms, and things like that, as a result, a lot of the data types can’t be as easily processed and organized using traditional data management tools and relational databases.
The huge volume of data at a high velocity is sometimes accompanied by noises. These noises compromise with the data quality and truthiness of data, as these noises can be malicious content and can make data prone to cyber-attacks by hackers.
The most important of all the V's is the value of big data because data is of high value only if the information gained after data processing is of much higher value than the cost of resources, time, and effort invested in it. The value of big data drives business decisions.
# Big Data Processing
As much as data warehousing is important for big data so is data processing. There are multiple open-source tools available today which help to process data in an ETL data pipeline. These open-source data processing tools include Apache Hadoop, Apache Spark, Apache Cassandra, Neo4j, and MongoDB.
Let's dig deeper in some of these tools:
It remains the most reliable solution for data processing. It can be run on-premise or in the cloud servers for data processing and has really low resource requirements. The main Hadoop features include HDFS (Hadoop Distributed File System), MapReduce (a highly configurable model for data processing), YARN (Hadoop resource management scheduler), and Hadoop Libraries.
Apache Spark is the successor of Apache Hadoop. It was built to address the limitations of Apache Hadoop. It can do both batch data processing and real-time data processing. It usually operates faster because of in-memory data processing compared to its brother Hadoop MapReduce, which leverages disk processing. Additionally, it can work fluently with HDFS, OpenStack, and Apache Cassandra, both in the cloud and on-premise data processing systems.
# Big Data Warehousing
As the name suggests, a data warehouse is a warehouse where large data sets from several data sources are first extracted to get relevant data, transformed and, then stored for data mining by data scientists in the future.
There can 2 types of data warehouse in general:
Traditional data warehouse
These are on-premise either SQL(MySQL, Postgres) or NoSQL databases which act as data warehouses. They are managed by IT and infrastructure teams of organizations and do, making them responsible for its scalability, maintenance, and performance.
Cloud data warehouse
These data warehouses services are offered by public cloud computing providers such as ** Amazon Redshift, Apache Hive**, and Google Cloud Platform. In these data warehouses, raw data sets are first stored in data lakes like Amazon S3 and then processed and transformed to get relevant data, which is then stored in cloud solutions like Amazon Redshift, in remote clusters of servers hosted on the internet, for future data analysis and data mining.
# Power of Big Data
The data analytics done on data sets by data scientists is a major game-changer in today's world. From the internet of things to machine learning and artificial intelligence, the benefits of Big Data analysis are already being realized. Let us see some of the big data applications.
Machine Learning & Artificial Intelligence:
Machine learning is a field of Artificial Intelligence that creates a system, which learns from data sets rather than through explicit programming. A machine-learning model is an output generated when a machine-learning algorithm is trained with data sets. After training, when the given model is provided with an input, it gives a result. The probability of getting accurate results increases with the amount of data sets used for training. Hence, big data acts as a key ingredient in making more and more accurate machine-learning models via an iterative training process, by providing a huge amount of data sets for training. For eg. a machine learning face recognition app is trained by feeding a large number of images of human faces so that it can correctly recognize a human face in the future.
Internet of Things:
IoT sensors are used for remote health monitoring of patients by doctors, global positioning systems for location tracking of delivery fleets, remotely controlled smart devices like remotely controlling smart bulbs & smart TVs. Internet of Things forms a continuous stream of data flow that feeds into the ocean of big data. The highly connected network of sensors, mobile devices, and smart appliances like smart TVs & smart refrigerators, form the IoT network, which makes a significant contribution to the volume of data collected for data Analytics.
Big Data Analytics & Predictive Analytics:
Big data analytics is usually a complex process of extracting, transforming, and loading(ETL) big data, to gain information such as hidden logical patterns, unknown correlations, logical reasoning for unknown behaviors and data trends, which helps organizations to predict user behaviors or outcome of a user action, to make better decisions. For eg. Gaining information from purchasing trends of previous customers, can help Amazon to show better recommendations to a customer who has just bought a TV, hence improving the overall customer experience.
Data mining is an intelligent mix of statistics and artificial intelligence(AI). Data mining is about analyzing big data using statistical tools and AI models to find unknown patterns. Generally, the need for data mining is to either classify new data sets or predict the behavior of new data sets. In classification, new incoming data sets are classified into groups to which the data sets match the most. In prediction, the behavior of new datasets is predicted based on training from old data sets. Typical data mining algorithms are Classification trees, Logistic regression, Neural networks, K-nearest neighbors clustering, K-means clustering etc.
# Moving Your Big Data, On The Fly!
As we know, a common form of data stored in Amazon Redshift clusters, are server logs. If you collect Big Data in a traditional data warehouse like MySQL, FlyData Sync can replicate your data onto your Redshift cluster by using the MySQL binlog. Once Sync is configured with your Redshift cluster, any change that is made in the binlog related to the data or schema of the tables that you did, is replicated to your Amazon Redshift cluster. With FlyData Sync, you can also automate the creation of reports and charts, as well as automated email alerts based on changes to your data, using the tools provided by Redshift.
With FlyData’s services, you can save valuable time and resources while increasing the value of your data. With faster and easier access to your Big Data, you can run real-time data analytics with ease and create valuable insights. To learn more contact us.