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.\r\n\r\nBig 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.\r\n\r\nThe 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.\r\n\r\nWith 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.\r\n\r\n \r\nWhy Big Data Analytics Is Vital?\r\nBig 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.\r\n\r\n \r\nCost Reduction\r\nBig 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.\r\n\r\n \r\nFast And Improved Business Decision\r\nWith 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.\r\n\r\n \r\nInnovative Products And Services\r\nGiven 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.\r\n\r\n \r\nBusinesses That Make Use Of Big Data Analytics\r\nBusinesses 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.\r\n\r\n \r\nBest 10 Open Source Big Data Solutions\r\nThe following are some of the best open source big data solutions in the market.\r\n\r\n \r\n1. Hadoop\r\nApache 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.\r\n\r\nThe 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.\r\nCharacteristics Of Apache Hadoop\r\n\r\n \tImproves authentication when utilizing HTTP proxy server\r\n \tProvides a specification for filesystem effort compatible with Hadoop\r\n \tOffers POSIX-style filesystem extended features.\r\n \tRobust bio-network is properly-suited to fulfill the analytical requirements of designers.\r\n \tFlexible Data Processing\r\n \tAllows faster data processing\r\n\r\n\r\n\r\n \r\n2.\u00a0Statwing\r\nStatwing 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.\r\nCharacteristics Of Statwing\r\n\r\n \tInstant data search or investigation\r\n \tData cleaning, correlation search, and instant creation of tables\r\n \tSupports the creation of histograms, scatterplots, heatmaps, and bar charts which are easily exported to Microsoft Excel or PowerPoint\r\n \tPresents analytic outcomes in clear and easy-to-understand English, furthermore, can easily be utilized by people who have little knowledge of big data analysis\r\n\r\n \r\n\r\n3. Apache Spark\r\nApache 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.\r\n\r\nApache 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:\r\n\r\n \tDistributed task transmission\r\n \tArrangement\r\n \tI\/O feature\r\n\r\nApache Spark is a substitute for Hadoop\u2019s MapReduce. Even so, it can run tasks 100 times faster than Hadoop\u2019s MapReduce.\r\n\r\n\r\n\r\n \r\n4. Apache Storm\r\nApache 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.\r\nCharacteristics Of Apache Storm \r\n\r\n \tCan process one million, one hundred bytes of messages for every seconds and every node\r\n \tHighly scalable\r\n \tFault-tolerant analytic solution\r\n \tProvides supports for many languages\r\n \tUtilizes parallel computations across a collection of devices\r\n \tAutomatic restart capacity\r\n \tEnsures every data unit goes through one or more processing-cycle.\r\n \tEasy-to-use big data analytic tools.\r\n \tInstant processing of data stream\r\n \tStorm scheduler allocates tasks to nodes.\r\n\r\n\r\n\r\n \r\n5. Kaggle\r\nKaggle 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.\r\nCharacteristics Of Kaggle\r\n\r\n \tGreat for discovering and flawlessly analyzing open data\r\n \tOffers users Search box to help them discover open datasets\r\n \tSupports open data connection with other data supporters\r\n\r\n \r\n\r\n6. Cassandra\r\nCassandra 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:\r\n\r\n \tConstant accessibility\r\n \tEasy operations\r\n \tEasy circulation of data centers\r\n \tCloud accessibility points\r\n \tScalable performance\r\n\r\nThe 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.\r\n\r\n\r\n\r\n \r\n7. RapidMiner\r\nIt 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:\r\n\r\n \tPreparation of Data\r\n \tSupports for varying levels of machine learning\r\n \tMining of texts\r\n \tPrognostic analytics\r\n \tDeep learning\r\n \tHigh level of analytic solution and big data technology\r\n\r\n\u00a0Characteristics Of RapidMiner\r\n\r\n \tSupports multiple data analytic solutions\r\n \tGUI or batch processing\r\n \tCollaborative and shareable control panel\r\n \tDistant analytic procedure\r\n \tData sifting, integration, linking and gathering\r\n\r\n \r\n\r\n8. Flink\r\nFlink is an Apache open-source and distributed big data technology with high performance. Likewise, it is always accessible and offers precise data streaming capacity.\r\nCharacteristics Of Flink\r\n\r\n \tOffers precise outcomes\r\n \tTasteful with fault-tolerant capacity\r\n \tCan recuperate from let-downs\r\n \tCapable of running extensive big data analytics on thousands of nodes\r\n \tHas a good amount of latency characteristics\r\n \tSupports stream processing and windowing with event time semantics\r\n \tFlexible window support\r\n \tProvides supports to a broad array of third-party system connectors.\r\n\r\n \r\n\r\n9. MongoDB\r\nMongoDB 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.\r\nPopular Features Of MongoDB\r\n\r\n \tCapacity to store different types of data set\r\n \tCloud database flexibility. Additionally, it readily divides data between servers in a cloud infrastructure.\r\n \tMongoDB utilizes forceful strategies that ensure instant procession of data.\r\n\r\n \r\n\r\n10. Pentaho\r\nPentaho 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.\r\nCharacteristics Of Pentaho\r\n\r\n \tEnables access to data and its integration to ensure efficient data insights\r\n \tEquip users to build big data source and carryout stream to ensure precise analytics results\r\n \tEasy switching of data or a combination of data processing of a collection of large data sets for optimal processing result\r\n \tData check capacity with readily accessible big data analytic solutions like graphs, visuals, and analytic reports\r\n \tSupports a broad range of big data sources through exclusive capacities\r\n\r\n \r\n\r\nThe Future Of Big Data Technology And Big Data Solutions\r\n\r\n \tMachine Learning is expected to grow more and assist businesses to collect large data sets and run analysis on them.\r\n \tGoing forward to privacy and data security issues are expected to get higher due to a large amount of data gathered from varieties of devices.\r\n \tWith 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.\r\n \tMany 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.\r\n \tMore 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.\r\n\r\n \r\n\r\nImportance of Big Data Solutions\r\nBig 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\u2019s decision-making capacity.\r\n\r\nAdditionally, 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.