Must have heard about data, right? Data is the form of characters, symbols, or quantities that is unprocessed and after going through all the operations it transforms into information. And then, you must have also heard about big data, no? When we talk about big data, it is the data in huge amounts as it can be identified from the name itself. It is a huge amount of structured and unstructured data that organizations use to extract useful information. Our technological world is no more alien to these concepts however there are three characteristics of big data that are volume, velocity, and variety which might be new to your knowledge. Volume refers to the handling and storing of a large amount of data in many environments, Velocity refers to how fast the organization is collecting and processing data to jump to instant outcomes and Variety refers to the types of data stored in data systems such as unstructured, structured and semi-structured data.
Big data technologies are used by organizations to improve their operations and better scalability and customer services. Embedding big data technology gives organizations a competitive edge in the consumer market because of the better decision making as they have a huge amount of data to work with.
Top Big Data Tools
Let’s say we are an organization that handles a huge amount of data. It could be like we run a social network or an organization that deals with public data in any other way. We do need some tools to help us in the handling and processing of data this huge. Well, what these tools do is help us in analyzing the data, processing it to find valuable insights and in storing it as well.
Now, when it comes to data tools, there are multiple things we have to keep in mind. For example the amount of data we have, the type of analysis we are going to do on it, and what outcome we are expecting out of it. Keeping all these intricacies in our minds, we are listing down the top five big data tools below.
- Apache Hadoop
Whenever we talk about big data tools there is one name that always pops up that is Apache Hadoop. It is a fully open-source framework that is used for large datasets. Hadoop brings self-scaling ability to your system when hardware failure is not an issue. It can virtually handle a very large amount of data. Apache Hadoop consists mainly of four things that are Hadoop distributed File System (HDFS), a programming model MapReduce, a platform YARN, and libraries that help Hadoop interfacing with other modules.
- Apache Spark
The successor of Apache Hadoop and the next big thing in the big data industry is Apache Spark. All the gaps left by Apache Hadoop in data processing were filled in by Apache Spark. It does not only rectify the shortcomings of Hadoop by processing batch data and real-time data but its model works a hundred times faster than MapReduce. Spark is a flexible framework as it can work with HDFS and other data stores such as OpenStack, Swift, etc.
- MongoDB
MongoDB is another open-source database that is based on NoSQL and has a lot of features. Its performance is the best when used for real-time data. Its compatibility is a prominent point as it is cross-platform compatible. It stores all types of data from text to integer to Boolean. Partitioning of data across multiple data centers is very easy in MongoDB that makes it flexible.
- Apache Cassandra
Apache Cassandra is a database that allows you to manage a large number of data sets across multiple data servers. This is the reason it manages a very heavy workload quite easily without any failure. It is always available as a data source because of its built-in high availability. Scalability in Apache Cassandra is very good due to the constant distribution of datasets across multiple nodes.
- Apache Storm
Storm, yet another great service by Apache is a real-time distributed framework with the capacity of fault tolerance. Any programming language is supported by this framework. Apache Storm balances the workload among multiple nodes and data centers just like Cassandra. Scalability in this framework is very good especially its horizontal scalability outshines many others. Its features like auto restarting when crashed and fault tolerance make it a good choice for organizations.
How to Use The Big Data Tools?
Let’s suppose we have a large amount of data and all the necessary tools to process and analyze the data. Does that solve the problem? No, it does not because having those tools is not beneficial until and unless we know how to use start working with these tools. We are listing down the ways to utilize these tools effectively.
- You need to be agile to work with these tools because only this way you can be up to date with the latest emerging technologies. With every new technology, there are some changes in customer’s needs and you have to update your data and analyzing process accordingly to satisfy customer’s needs.
- You should be well aware of programming languages that are used in big data tools.
- You should always do business in real-time as it is the best way to analyze the experience of your customers. Using real-time data is the best option for that and by doing this you can get to know if there is anything that needs to be changed for better.
- You should be using different platforms to collect data such as smartphones, tablets, and laptops because a client can use anything to access products.
- All of the data should be used in analyzing the process, more the data better the outcome.
Now is the time when our traditional ways of data analysis are failing as they cannot cope with big data management Not only the data has become so big, but also there are different types of data such as structured data, semi-structured data, and unstructured data. There is always a need for big data experts to handle data this complex. The best way to become a data expert is to enroll in a data science academy's Big Data training. As far as we can see, data is only going to get more complex. So, having any of the aforementioned robust tools up your sleeves can save you big in this BIG DATA world.