Top 15 Big Data Tools 2019

There are plenty of big data tools are available in big data industry, but it is very important to choose a tool that would require little setup cost and would bring clear cut insights.  For those Businesses and developers who are looking for emerging big data tools, this blog will explore the best data analytics tools of 2019 here below. Check out the list, and choose the best tool that matches your business.

The following contents are originally published from Top 20 Big Data Tools 2019

Top Big Data Tools

1. Apache Hadoop
2. Apache Spark
3. Apache Storm
4. Tableau
5. Apache Cassandra
6. Flink
7. Cloudera
8. HPCC
9. Qubole
10. Statwing
11. CouchDB
12. Pentaho
13. OpenRefine
14. RapidMiner
15. Data Cleaner
16. Kaggle
17. Hive
18. Kafka
19. Graph Databases
20. Elastic Search

Read more about the Tools reviews , Features and their working strategy here below.

Top 20 Big Data Tools 2019

Top 7 Big Data Use Cases in E- Commerce

Big data Use cases in E-Commerce Industry

#1. The use of Predictive Analytics

This analytics can be used to analyze what will be the trend and what will create a buzz in social media by predicting the future with forecasting algorithms. Also, predictive analytics is used to determine what a  customer can buy in future this can be known as sentimental analysis, which will analyze what a customer discussing e-commerce products on social media.

#2. Optimizing the Product Price  

Using real-time analytics in big data can help retailers to enable the best price for goods by tracking through the transactional records, competitors, and other things. So this is why the pricing of a particular product in an e-commerce store often varies. 

#3.Helps to Forecast the Demand

For websites like Amazon, it is very important to execute accurate forecasting on demand, because it is very complex to manage their inventories on shelves. Amazon uses some real time forecasting tools to track the historical data and those tools will have the provision of assessment in demand fluctuations. 

#4.To bring Better Customer Experience 

I. 89% of customers are refused to buy a product in an e-commerce portal, after experiencing poor service with them. 

II. 83 % of customers often require immediate customer support while purchasing a product online. 

Big data lets the business to create a 360-degree view on users in order to bring stressless and optimized customer experience by compiling the previous online/offline transactions, social media discussions, product reviews and etc.

#5.To create Personalized Stores : 

Big data can help e-commerce businesses to create dynamic websites that are filled with the relevant products by tracking the previous purchase history and browsing details of a customer.

#6.To Increase Conversion Rates and sales 

Big data can be used to personalize the purchase by avoiding the cart abandonment. A statistics report that a  huge volume of customers has been failed to make purchases at the last minute, even the product has been listed into the cart.  Businesses and retailers can use big data to offer personalized customer experience in order to prevent such cart abandonment.

#7.Can increase the decision making on micro-moments

Micro-moments are the trend and hot topic on the e-commerce industry last year, and this will be the trend of 2019 and 2020 too. Every customer looks for an immediate solution while purchasing. Around 70% of sales are made through smartphones. So retailers focused to improve the micro-moment decisions by connecting the smartphone technologies with big data analytics. Refer : medium.com

Conclusion

It is clearly shown that big data is directly impacting the increase in sales conversion, increase in customer experience, increase in revenue through advertisements, and finally increase in ROI. As listed above there is ‘N’ number of use cases can be derived while using big data in e-commerce. We have mentioned the top use cases of big data in e-commerce. 

Read more at : how big data can be used in e-commerce industry

How Big Data is Being Used In Facebook?

Nowadays, it is impossible to see a person without not connected with an social media. Because the world is getting exponential growth digitally around every corner of the world. According to a report,  2.77 billion peoples are using social media in 2019. In 2021 the count will be nearly 3.02 billion. 

Sure the world will achieve this count before 2021, because we all are using social media, without concentrating our daily work, like eating, sleeping or whatever. also the number of mobile users also has been increased when compared to the previous years.

This drastic growth of social media is directly impacting the data generation. Yes, Whatever we do in social media including a like, share, retweet, comments and everything has been stored as a record, and which has been generated data. 

So what kind of strategy that businesses like Facebook have decided to handle all this data? 

To handle all this data, Organizations like Facebook have adopted BIG DATA technology. Here we gonna discuss how Facebook is using big data analytics? Why they are using big data analytics. Let’s discuss more.

How Big Data is Used in Facebook?

The main business strategy of Facebook is to understand who their users are, by understanding their user's behaviors, interests, and their geographic locations, facebook shows customized ads on their user's timeline. How it is possible?

There are around billion levels of unstructured data has been generated every day, which contains images, text, video, and everything. With the help of Deep Learning Methodology ( AI), Facebook brings structure for unstructured data. 

A deep learning analysis tool can learn to recognize the images which contain pizza, without actually telling how a pizza would look like?.  This can be done by analyzing the context of the large images that contain pizza. By recognizing the similar images the deep learning tool will segregate the images that contain pizza. This is how data Facebook is bringing a structure to the unstructured data. 

Read more at How facebook using big data?

How Does Big Data help For Business Growth?

Data sets that are too large to handle are known as big data. For enterprises and large scale businesses, it becomes a complex task to segment the huge volume of raw data.  So to rectify these issues Big Data have been introduced.

What is Big Data?

Big Data is a systematic data management process that treats a comprehensive approach with a high volume of both structured and unstructured data in order to bring better data insights.

Business intelligence helps business to visualize the limited amount of structured data that worked and not worked in the past. Big Data, let the entrepreneurs experience the exact business insights that will bring fruitful results in the future. Big Data with predictive analytics tells what steps should be made in the future to drive the business on the right path.

Big Data Analytics

It is a systematic approach. It examines the large variety of data sets, to unbox the hidden patterns, market trends, customer experience, data complexities, and business flow to make better-informed business decisions.

How big data helps in business growth?

None of the business launched to the market with a big amount of data in the initial stage, when the business grows as a brand, the data behind the business flow gets dumped in huge volume. So, Big Data is strictly suitable for large scale businesses that are in big need to manipulate or audit their business information.

Why business has to manage the data?

There are two reasons,

1. To segmentize in an easily readable, identifiable, trackable data, which we call as data modeling

2. To make better decisions, by analyzing the data and visualizing it with proper data visualization process.

If a business fails to do any one of the above, sure it will be tough for them to lead the business next level and will be tired of trapped into the data collections.

Data Analytics Processes

Big Data Analytics involves the following basic processes.

1. Data aggregation   -  Data collection.

2.  Data Cleansing      -  Filter out unwanted data.

3.  Data Modelling       -  Making the Data into easily identifiable data sets.

4.  Data warehousing  -  Storing the data into the warehouse.

5.  Data Visualization  -  Visualizing the data in a graphical manner.

6.  Visual Report Development - Developing visual reports.

In Future, we will discuss each process in detail.

Originally published on  : How Does Big Data help For Business Growth?