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What is Big Data?
Understanding what big data is is important to move on the visualization of it. The most popular definition is known as three Vs, “Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity“. Without these three, a large data set cannot be categorized as big data. Because the volume of the data is not such a big problem anymore.
Every single day big data is evolving. Lately, we have two more Vs such as value and veracity. As you can see, there is no limit when it comes to data. However, in this post, we will not go further with the definition, yet you can read more on Oracle.
Importance of Big Data
When something enters our daily life and makes things easier, we welcome its existence and start to feel it as a necessity. Big data is around for some time now and you cannot do anything to discard it anymore. Even you do not notice it around, there are lots of use cases such as mobile phones, Netflix, transportation, energy, healthcare, and much more. But the importance comes mostly from big data analytics. Because the results are used in various and unlimited areas. Most decisions are results of these analytics, and we are making decisions of decisions with the help of big data.
Where do we use Big Data?
Let’s see big data in practice. The below list is the headlines but you can get the idea. If you want to see further explanations, here they are.
- Profiling Customers: We are understanding customers and their needs, so we can advertise things better.
- Business Processes: Big data helps use for Understanding and Optimising the processes, so we can increase production and reduce the waste of effort and money.
- Personal Analysis: Thanks to wearable devices now you can race with anyone. Improve your performance, etc.
- Healthcare Improvements: Patterns in biomedical data helps us to detect anomalies.
- Sports: Usage of big data in sports is vital for players’ health and performance. From weather conditions to eating habits, from grass to the fitness metrics.
- Improving Science and Research: Technology is the limit here. Your processing power grows every second.
- Optimizing Machines: We are training self-driving cars with big data analysis.
- Better Public Security: Law Enforcement uses big data to provide a more secure environment for us.
- Improving Cities/Countries: Better understanding and improvements in public transportation, traffic flow, wifi-hotspots, lighting, etc.
- Financial Trading: Pricing in the market is continuously changing and you can get notifications for buy-sell decisions.
My personal favorite is the improvements in healthcare. Knowledge in this area is limited but growing steadily. Applications are released day by day in this field and wearable IoT devices help us for more.
You can easily conclude that diversity in the usage areas and the amount of these large data sets causes lots of junk data. Somehow we have to deal with that and the tool we know is big data analytics.
Big Data Analytics
Using advanced analytic techniques against diverse and large data sets from different sources can be named as big data analytics. This definition implies that every single source on the internet can say different application areas regarding big data analytics.
Every single application or company collects the data according to the needs of the business domain. For example, think you are surfing online. Google collects your data and runs an analysis to provide more accurate results within your region. On the other hand, Adsense feeds its system for suitable advertisements for you. Another log collector might be your Internet provider. They can block your access to malicious sites blacklisted by network analysis tools.
Visualizing Large Data Sets
Well, data visualization makes it possible to understand data sets more easily. When it comes to large data sets even visuals can have limitations. For example, think you want to display the locations of every single person on the map. And you have a 4K monitor (4096 x 2160) that has almost 9M pixels. So, you can only show half of the New York(20M) population. Visualization in big data may become easier with the help of the following tips.
Step 1 – Discard Unnecessary Data (Map Reduce)
Large data sets are usually multidimensional. This makes the size of the collection bigger and this means ineffective usage of resources if you are not using all the dimensions. Technically speaking, you are allocating memory and processor with garbage. Your first move should be eliminating those unused dimensions. Then, you should use queries and filters to focus on the information you have to visualize. Once you discard the unnecessary data, you can work on the reduced data size compared to the beginning. This process is defined as a programming model called Map Reduce. It is the way of dealing with big data.
Step 2 – Use Basic Visualization Tools
Even if you follow the above instructions, you will still have a big data set. Now, you have to work with big data analytics on your set. According to your needs, these tools provide useful summaries and you can visualize them easily with basic visualization tools. For insights, you do not always need complex next-generation tools. Start small, then the rest will be easier.
Step 3 – Link Basic Views
Once you have basic visuals, the next thing you should do is connecting them to show relations. Linked views always provide a better understanding of the domain. A combination of different aggregations on a specific domain can tell more about your collection.
Step 4 – Add Interactivity
This step is the cream of it. In a report, you don’t need interactivity. But if you are providing data dashboards for administrators, data analysts, etc., interactive visuals would give more information than a single static view.
Top 3 Big Data Visualization Tools
There is no magic behind big data visualization as you can see with the above examples. Software that can handle more visual items with solid backend infrastructures is known as big data visualization tools. Visualization for big data is driven by the competition between the following products.
- Customizable dashboards integrated with Salesforce, SharePoint, etc.
- Real-time interactive dashboards featured with drill-down
- In-memory data with various data sources
- Mobile optimized
- Interactive dashboard with real-time data feed and easy sharing
- Customizable reports
- Sharable Datasets
- NLP support for your queries
- Embedded Analytics Integrated with Python
- Customizable Dashboard
- Predictive Analysis
- Free and Open Source
- Needs SQL Knowledge
- Easy Learning
- Interactive Querying