What are Big Data platforms and Big Data technology? Well, Big Data platforms and Big Data technology are two terms that are often used interchangeably.
A big data platform is an architecture that contains servers, storage, databases, business intelligence, administration, and management services for managing Big Data. It strengthens customized development, querying functions, and integration of diverse processes. The main advantage is to bring down the complications of different solutions and vendors into one comprehensive big data platform. Such a platform is also made available in the cloud through which a provider gives comprehensive big data services and solutions.
The big data technology ecosystem most often commences with Hadoop, and also includes other platforms for processing big data. This write-up provides knowledge about big data platform and their technology and their deployment for your understanding. Many platforms of Big data flourish with both advantages and disadvantages for their users. A range of technologies such as Hadoop clusters, NoSQL databases, Spark engine for processing, and even a data warehouse and a traditional database can make an architecture for big data platform.
However, for meeting the respective business goals, it is up to the technical and management involved in setting up a framework for a big data platform and its deployment to keep themselves on right track. Designing a big data ecosystem can be compared to designing a multi-lane and multi-level bridge to deal with diverse traffic demands. In both situations, one needs to prepare for future usage that is also customizable on the same foundation to make it hold emerging new platforms as well as tools needed for growing business demands.
The agility of technology is a key factor in a carefully designed big data architecture. Also, related attributes like capabilities of rapid deployment and linear scale-out, and support for schema-on-read access to data modeling provide flexibility to organizing information. As all data is not equal, we require treating various data in different ways. To focus on such challenges, many businesses usually deploy many big data platforms to deal with various parts of the processing. The alternatives include software tools like Spark, Hadoop, and database technologies. A road map can help steer the selection process of technologies, and real time programs for big data with the available trending information on big data technology.
Adopting an appropriate big data platform
Hadoop looks interchangeable with big data. It is a key component in most big data architectures. However, the ecosystem of big data technology expanded to encompass diverse platforms that are enlarging Hadoop in user deployments or replacing it in some situations altogether. The expanding alternatives in the technology make businesses more flexible to meet the needs of their applications. It further builds upon the batch processing of Hadoop to facilitate real-time analytics and flow processing.
Big data platforms and operating control systems
Like in any other IT project, big data applications also face many hurdles sometimes larger than anticipated. These can be associated with designing, planning, and building big data architecture, configuration, partitioning data sets, deployment of tools for advanced analytics, data governing, controlling the operations of Hadoop clusters, and other platforms related to big data.
Development of technology on big data platforms
Events change swiftly in the ecosystem of big data partially due to the open source structure of Hadoop, Spark, and similar technologies. Also, many platforms of big data and tools are comparatively new and get updated with newer functionalities on a constant basis. The cloud computing growth and emerging technologies like micro services and containers are also forcing advances in big data software and systems. In this context, a 2016 report by Forrester Research evaluated some twenty-two technologies in the whole life cycle of big data.
The Forrester Research analysis indicated some of the best big data technologies listed below.
1. Stream analytics – Software to clean, aggregate, analyze and refine a large output of data from heterogeneous sources of live data and in any format of data.
2. NoSQL databases: Chart and graph databases, document, key values.
3. Data preparation: Software tools that aid in sourcing, cleansing, shaping, and distributing various and disorderly sets of data for accelerating analytics for insights.
4. Predictive analytics: Analytics of sources of big data with predictive models using hardware and software solutions that support a business to evaluate, discover and optimize the performance and mitigate risk.
5. Investigation and knowledge exploration – Technologies and tools to support extracting information to get new insights from huge storehouses of structured and unstructured data residing in multiple sources like databases, APIs, file systems, streams, and other applications and platforms.
6. Visualizing data – An automation that displays information taken out from different sources of data, including sources of big data like Hadoop and shared data stores in real time.
7. In-memory data framework– Gives low-latency connection and processes huge data volumes by sharing data through DRAM(Dynamic Random Access Memory), SSD of a shared computer system, or Flash.
8. Shared file stores – A computer grid where data is saved on more than one node, usually in a duplicate for attrition and performance.
9. Integrating Data – Data integrating tools throughout the solutions like MongoDB, Hadoop, MapReduce, Couchbase, Apache Spark, Amazon Elastic MapReduce (EMR), Apache Pig, and Apache Hive.
10. Data quality software – To manage cleansing of data and its enhancement on wide varieties of datasets with high-velocity by running parallel processes on distributed databases and data repositories.
In addition to the above, this write-up attempts to brief about a few big data technologies, databases, and repository and cloud technologies.
The prominent implementation in MapReduce is Hadoop, which is a completely open source platform for Big Data. It is adequately flexible and enables aggregating multiple data sources for processing on a large scale, or data reading from a data source to go through processor-intensive machine learning tasks. It allows many distinct applications of which the leading use case belongs to constantly changing large volumes of data, like locational data such as traffic sensors, or whether, or social media data or web-based, or machine-to-machine data of transactional nature. Hadoop is evolving with more security designs such as Rhino, Sentry, etc. Once they gain stability, the implementation of Hadoop can spread through many more businesses without security worries.
MapReduce supports the scalability of tremendous work execution through hundreds and thousands of servers or an innumerable number of clusters of servers. MapReduce has two functions in its implementation. The first task is “Map”, where data set input is transformed into sets of different pairs of key/values or tuples. In the second task, the “Reduce”, where various outputs of the task “Map” are connected to create a set of reduced tuples.
Complementing Hadoop, Apache Spark is the fastest engine for big data processing. Spark speeds up computational processing of Hadoop. Spark makes use of Hadoop for processing and storage. It also reinforces Big Data languages like Scala, Python, R, and Java. Apache Spark is mainly aimed to track real-time fraudulent transactions.
Flink, an open source structure, streams data quite accurately with high performance. The technology of MPP databases like Query Optimizer, in-memory algorithms, influenced Flink’s competence. Hadoop MapReduce has also influenced Flink’s functions like Schema on Reading, User Defined Functions and Massive scale-out functions.
Kafka works as a glue between different processes such as Spark, NiFi, and some other third party tools. Kafka, also an open source platform, handles data streams in real time with efficiency. It is scalable horizontally and is very fast and fault tolerant. Kafka keeps messages in the topics in storage as a distributed process, where the topics are reproduced and partitioned throughout various nodes.
Hive is just like SQL bridge that connects traditional application of business intelligence to go through queries in a cluster of Hadoop. Originally developed by Facebook, it became now an open source, and Hadoop framework higher-level abstraction tool supports making queries on data stored on a Hadoop cluster, just like on a traditional data store. Hive strengthens Hadoop’s reach to users of business intelligence.
Similar to Hive, PIG is also another type of bridge to bring closer Hadoop to the business users and developers alike. However, in contrast to Hive, PIG comprises a language similar to Pearl that makes querying across stored data on a cluster of Hadoop instead of a language like SQL. Yahoo developed PIG and made it open source.
In WibiData, Hadoop is combined with web analytics built on HBase that itself is a database layer of Hadoop. Its purpose is to make websites work with users and explore their data, facilitating responses in real-time to users, like providing personalized content, decisions, and recommendations.
Hadoop has the biggest limitation in that it is a small-grade execution of MapReduce. It requires considerable knowledge on the part of a developer to manage. Hours can run by to complete a full cycle of preparing, testing, and driving jobs without the user interactivity, normally associated with the traditional databases. Platfora turns queries from users into automatic Hadoop jobs, setting up a generalized layer for anyone to use for simplifying and organizing stored datasets in Hadoop.
Conventional and row-based databases are good for processing an online transaction but fall short on data query processing as the volumes of data grow and become more unstructured. In comparison, the column-based database allows the compression of huge data with a very quick query time. The only disadvantage with such a database is its limitation on batch updating time that is often lower than the conventional models.
NoSQL databases, or Schema-less databases
Many types of databases fall in this group, such as document stores, and key-value stores, that concentrate on the repository and recovery of huge volumes of semi-structured or unstructured, or structured data. They do away with the restrictions usually associated with the traditional databases like read-write consistency, exchanging scalability for distributed processing.
Along with the growing volumes of data, the need for effective and efficient storage techniques is also growing.
A data analytics tool and a machine learning tool of high-performance, Sky Tree specifically focus on dealing with Big Data. Machine learning is essentially a part of Big data, making exploration of data volumes of massive nature easy by eliminating the need for unfeasible and expensive traditional or manual exploration.
Big Data in the cloud
Most of the big data technologies are linked closely with the cloud solutions, Many cloud vendors offer hosted clusters of Hadoop capable of scaling on demand as per the needs of a user. Many of the tools and platforms mentioned in this write up are either cloud-based versions or completely cloud-based.
Cloud computing and Big Data perhaps are inseparable. Cloud computing makes businesses of any size derive data value for their operations more than before with much-reduced costs compared to their earlier times. This enabled them to store more and more data thereby making more and more need for more processing power.
Cloud solutions continue to influence Big Data solutions
With IoT gaining the center stage, generating data is also increasing. And IoT applications need scalable solutions to manage voluminous data. Cloud solutions are the perfect option for this. Many businesses have already realized the advantages of Hadoop on the Cloud. Technologies around Big Data like Hadoop, IoT, Spark and Cloud continue to grow in the future also.
Real-Time Solutions are set to expand.
Companies know how to store and process Big Data. The real issue is how fast the analytical solutions can be delivered, and in 2017 the focus is on the speed with which data can be processed. And the speed of Processing power is set to increase. Software products like Kafka, Storm, Spark, etc. are pointers in this regard.
The ecosystem of big data is continuously expanding with new technologies emerging periodically. Many technologies are growing outside Hadoop-Spark. You can emerge as an expert in Big Data with specialized courses such as Hadoop, Storm, Spark, Cassandra, and Mongo DB to name a few.
Become a Big Data Expert
You can become a Big Data expert with the Big Data Specialization course that covers Hadoop, Spark, Storm, MongoDB, and Cassandra. Big Data analytics and tech training can advance your career. A sharp focus on domain-specific use cases can make you productive.