Big data means a large set of data which is very complicated for standard data tools and technologies to manage. In addition, the popularity of big data solutions and big data analytic tools escalated with the advent of wireless connectivity, mobile technology, Internet 2.0, the Internet of Things, and other emerging technologies, given the huge amount of big data that users of these devices generate.
Big data solutions are used to evaluate large amounts of data to discover unseen patterns, constructions, and other more insights to improve business decisions and offer a better solution to customers. In fact, with the latest big data technologies, it is possible to analyze the collected data and obtain an immediate answer that will inform a better business decision compared to using standard business intelligence tools.
The major advantages of these new big data technologies over the traditional analytic solution are the fact that it is faster and more efficient. While some years ago, when businesses collect data and conduct analytics, they gather the information they would use to make future business decisions.
With the modern-day big data analytics, however, the insights uncovered from running analytics are utilized for immediate business decisions. Additionally, the speed and the agility factor that it brings to table help organizations to maintain a competitive advantage.
Why Big Data Analytics Is Vital?
Big data analytics assist businesses to discover fresh opportunities. Furthermore, this helps them to make smart business decisions, operate more efficiently, and improve their profits. At the same time, it helps to ensure optimal customer satisfaction.
Cost Reduction
Big data analytics technologies like Hadoop and cloud-created analytics help business to the reduced cost involved in data storage. Moreover, they help them discover a better way of managing their businesses.
Fast And Improved Business Decision
With the fast result delivered by Hadoop, together with in-memory analytics, coupled with the capacity to analyze fresh data sets, businesses can now leverage on this tool to get an instant solution which they can implement to make better business decisions.
Innovative Products And Services
Given the capacity to estimate the needs of customers that comes with the use of these big data analytics technology, businesses ensure customer satisfaction. After all, innovative business solutions are developed to fulfill the needs of consumers. Today, the success recorded by large tech companies like Google and Facebook demonstrate that a good data management strategy can make a great impact on business success.
Businesses That Make Use Of Big Data Analytics
Businesses that maintain a competitive edge through fast and agile decisions need big data analytics technologies to achieve their goals. Additionally, this includes businesses in the following industries life sciences, banking, manufacturing, health care, government, and retail.
Best 10 Open Source Big Data Solutions
The following are some of the best open source big data solutions in the market.
1. Hadoop
Apache Hadoop is the most popular and widely utilized big data analytics technology in the market today. Moreover, it has a massive capacity to process a significant amount of data. Likewise, Hadoop is completely an open-source platform which runs on service hardware of a data center. Additionally, it can as well run on a cloud-based platform.
The collection of software in Hadoop makes distributed processing of huge amount of data sets through groups of computers possible. Furthermore, it is structured to step up from one server machines up to thousands of server machines.
Characteristics Of Apache Hadoop
- Improves authentication when utilizing HTTP proxy server
- Provides a specification for filesystem effort compatible with Hadoop
- Offers POSIX-style filesystem extended features.
- Robust bio-network is properly-suited to fulfill the analytical requirements of designers.
- Flexible Data Processing
- Allows faster data processing
2. Statwing
Statwing is a user-friendly big data analytics technology created by big data analysts to make their analytical tasks easy. Also, it comes in a contemporary interface that is automated to select statistical analysis.
Characteristics Of Statwing
- Instant data search or investigation
- Data cleaning, correlation search, and instant creation of tables
- Supports the creation of histograms, scatterplots, heatmaps, and bar charts which are easily exported to Microsoft Excel or PowerPoint
- Presents analytic outcomes in clear and easy-to-understand English, furthermore, can easily be utilized by people who have little knowledge of big data analysis
3. Apache Spark
Apache Spark is another popular open-source big data analytics tool and data management technology example. The main benefit is to make up for the data processing gap of Apache Hadoop. Furthermore, Apache Spark can process both the collection of data sets and current data. Besides, it carries out in-memory data processing, which yields a faster result than a standard disk processing.
Apache Spark is compatible with HDF, OpenStack Swift, and Apache Cassandra due to its flexibility. In fact, you can equally run Spark on one native system to enhance development and simplify analytic processes. Spark’s features include the following:
- Distributed task transmission
- Arrangement
- I/O feature
Apache Spark is a substitute for Hadoop’s MapReduce. Even so, it can run tasks 100 times faster than Hadoop’s MapReduce.
4. Apache Storm
Apache storm is another popular open-source big data analytics technology. Furthermore, it is a free and distributed platform that supports instant programming and fault-tolerant big data solutions.
Characteristics Of Apache Storm
- Can process one million, one hundred bytes of messages for every seconds and every node
- Highly scalable
- Fault-tolerant analytic solution
- Provides supports for many languages
- Utilizes parallel computations across a collection of devices
- Automatic restart capacity
- Ensures every data unit goes through one or more processing-cycle.
- Easy-to-use big data analytic tools.
- Instant processing of data stream
- Storm scheduler allocates tasks to nodes.
5. Kaggle
Kaggle is one of the biggest types of big data community across the globe. Additionally, it assists businesses and research analysts to analyze their big data. Furthermore, it is a good big data solution for seamless analysis solution.
Characteristics Of Kaggle
- Great for discovering and flawlessly analyzing open data
- Offers users Search box to help them discover open datasets
- Supports open data connection with other data supporters
6. Cassandra
Cassandra is a broadly used distributed big data analytics technology that manages big data set across many servers. Likewise, it is among the best big data tools for processing structured data. Moreover, Apache Cassandra’s big data solutions offer highly accessible service without a particular point of failure. Also, it has peculiar characteristics not found among interactive and NoSQL databases. These characteristics include the following:
- Constant accessibility
- Easy operations
- Easy circulation of data centers
- Cloud accessibility points
- Scalable performance
The nodes of Apache Cassandra big data solutions perform an identical role. Besides, it can manage many simultaneous users from different data centers. Thus, it is easy to integrate a fresh node in the available collection of data even during up times.
7. RapidMiner
It is another popular open-source big data tool and technology utilized for the preparation of statistics, model distribution, and machine learning. As a matter of fact, RapidMiner is a new big data technology utilized by data scientists and analysts for performing the following functions:
- Preparation of Data
- Supports for varying levels of machine learning
- Mining of texts
- Prognostic analytics
- Deep learning
- High level of analytic solution and big data technology
Characteristics Of RapidMiner
- Supports multiple data analytic solutions
- GUI or batch processing
- Collaborative and shareable control panel
- Distant analytic procedure
- Data sifting, integration, linking and gathering
8. Flink
Flink is an Apache open-source and distributed big data technology with high performance. Likewise, it is always accessible and offers precise data streaming capacity.
Characteristics Of Flink
- Offers precise outcomes
- Tasteful with fault-tolerant capacity
- Can recuperate from let-downs
- Capable of running extensive big data analytics on thousands of nodes
- Has a good amount of latency characteristics
- Supports stream processing and windowing with event time semantics
- Flexible window support
- Provides supports to a broad array of third-party system connectors.
9. MongoDB
MongoDB is a NoSQL analytics tool that supports many platforms. It is an open-source tool and features a lot of built-in features. Moreover, it is a perfect big data technology that requires a fast and instant analytic solution to enable them to make fast business decisions. Additionally, MongoDB is compatible with Java, MEAN software stack, and NET apps.
Popular Features Of MongoDB
- Capacity to store different types of data set
- Cloud database flexibility. Additionally, it readily divides data between servers in a cloud infrastructure.
- MongoDB utilizes forceful strategies that ensure instant procession of data.
10. Pentaho
Pentaho is one among many big data solutions for extracting, preparing and combining a large amount of data. Furthermore, it provides insight and analytic results for altering ways of managing businesses. Additionally, this big data analytics technology helps businesses to uncover big business insights from big data.
Characteristics Of Pentaho
- Enables access to data and its integration to ensure efficient data insights
- Equip users to build big data source and carryout stream to ensure precise analytics results
- Easy switching of data or a combination of data processing of a collection of large data sets for optimal processing result
- Data check capacity with readily accessible big data analytic solutions like graphs, visuals, and analytic reports
- Supports a broad range of big data sources through exclusive capacities
The Future Of Big Data Technology And Big Data Solutions
- Machine Learning is expected to grow more and assist businesses to collect large data sets and run analysis on them.
- Going forward to privacy and data security issues are expected to get higher due to a large amount of data gathered from varieties of devices.
- With the generation of more data, there would be extra demands for data scientists and analysts. As a result, the annual income of data analysts and scientists will shoot up.
- Many businesses will want to utilize big data analytics solution. This will result in the emergence of many big data technologies. Consequently, many developers will create big data analytic solutions that will ensure businesses make precise business decisions from the data they gather.
- More businesses will know how effective, valuable, and lucrative it is to make use of big data tools and technologies. And so, they start to make use of it.
Importance of Big Data Solutions
Big data solutions make it easy for businesses to gain a competitive advantage. Moreover, they help by unearthing insights that could inform business decisions. Besides, there are many types of big data analytics tools that businesses can leverage to analyze big data to ensure agile and real-time business decisions. Despite these big data solutions, there are also innovative big data analytic technologies. Therefore, they play a huge role in an enterprise’s decision-making capacity.
Additionally, they help businesses to develop fresh business ideas and make well-calculated business decisions. Furthermore, the future of business may be entirely tied around big data analytic solutions. Going forward, big data solutions offered by data scientists and big data analysts will continue to determine how we store, transfer, and recognize data.
