What is Big Data – The Big data is nothing but a databank available on the Internet and in the enterprise. Big data is getting bigger, more confusing and difficult to process. More and more technologically sophisticated tools and programs are designed to tame the flood of data.
What is Big Data?
The term Big Data comes from the English speaking world. It only perceived as a phenomenon or a hype, the experts now summarize two aspects under this term. Accordingly, on the one hand the ever more rapidly growing amounts of data; on the other hand, it is also about new and explicitly powerful IT solutions and systems that companies can use to deal with the flood of information like machine learning in particular, unstructured data.
For example, from social networks – make up a not inconsiderable part of the mass data. With grid computing In this regard, a special form of distributed computing is now available, which enables computer-intensive and data-intensive data processing.
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Big Data – A New Era of Digital Communication
In general, the word creation Big Data is often used as a collective term for modern digital technology. But not only the digital amounts of data are in focus. Rather, big data and the associated digitalization also have a lasting influence on the collection, use, exploitation, marketing and, above all, the analysis of digital data.
In the meantime, this name stands for a completely new era of digital communication and corresponding processing practices. From a social point of view, this fact is even blamed for fundamental social change or upheaval.
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Generate Competitive Advantage with Big Data Analytics
However, this development is having an impact on the corporate landscape. Indeed, with the vast amounts of data available, companies gain new insights into the interests, buying behavior, and risk potential of customers as well as potential interested persons.
So that the information can also be filtered, examined, evaluated and classified appropriately, companies specifically use analytics methods. The term analytics conceals explicit measures to identify unknown correlations, hidden patterns, and other useful information in the datagrid.
These insights can then provide competitive advantages over competitors or bring other business benefits – such as more effective marketing or revenue increases.
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Software Tools for Advanced Big Data Analytics
With the complex data analysis , the companies primarily pursue the goal of creating better decision-making bases for their own business activities. To realize this main goal, a data scientist – the big data experts – evaluates huge amounts of transactional data as well as other information from a variety of data sources.
These sources include, for example, internet clickstreams, web server logs, mobile phone itemized records, sensor information, and, most importantly, user social media activity reports. For the processing and analysis of this mass data, companies use software tools that comprehensively facilitate both big and small data analytics.
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Open Source Software Frameworks for Big Data
In recent years, a whole new class of extremely powerful technologies and programs has emerged. The focus is on open source software frameworks such as Apache Hadoop, Spark , NoSQL databases and, for example, Map Reduce.
Especially Spark and especially Hadoop enjoy an immense popularity. Hadoop is based on the MapReduce generated by Google Algorithm combined with suggestions from the Google file system.
Users can use this program to process large amounts of data in intensive computing processes on so-called computer clusters; This process is also referred to as cluster computing. The development in this area is constantly driven by software companies, such as the providers Cloudera or Hortonworks.
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BigTable, Graph Databases and Distributed File Systems
For example, BigTable, the high-performance database system developed by Google, is becoming more and more important. Even the easily structured, distributed database management system Cassandra comes as an explicit solution for very large structured databases increasingly in the foreground. Cassandra is especially designed for reliability and high scalability.
Another alternative solution is represented by graph databases. Highly networked information is represented as graphs, whereby the specialized graph algorithms considerably simplify complex or complicated database queries.
In addition, it is advisable to use a distributed file system – a distributed file system . Such a network file system significantly optimizes the access and storage options.
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Optimized storage technology
In addition to modern and highly functional software, the hardware – explicitly the memory technology – plays a decisive role in big data. Meanwhile, the storage technology makes it possible to keep data volume in the context of so-called in-memory computing directly in the main memory of a computer.
In the past, this data usually had to be swapped out to slower storage media such as hard disks or databases. Thanks to in-memory computing , the computing speed is now significantly increased and real-time analysis of large volumes of data is possible.
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What is BigTable?
BigTable is a database system developed by Google. It is suitable for storing large amounts of data at high throughput and low latency. Google uses BigTable for its own services, but also offers it as a cloud service for third parties.
Google BigTable is a proprietary database system developed by the US company Google for extra-large data volumes. The extremely scalable NoSQL database system works on distributed cluster systems and offers high performance. It was originally designed to capture the huge petabytes of data generated by web services and Google Search and beyond.
Google uses BigTable for its own services, such as Google Search, Google Analytics , Google Maps, or Gmail. With low latency and high data throughput, Google BigTable is also suitable for other Big Data applications . The database uses a deliberately simple data model based on row and column timestamp entries. In addition, compression algorithms are used to store the data.
BigTable was developed in 2004. Although it is a proprietary solution from Google, BigTable has a major impact on the design of databases for big data applications. Based on Google’s published BigTable specifications, other companies and open source teams have been able to develop their own database systems with similar functionality and structure.
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The Working Principle of Google BigTable
The BigTable database is based on tables, which in turn consist of rows and columns. Lines can have a differing number of columns and are indexed by the row key. Each line can consist of columns with individual values.
Columns that are related to each other are grouped in column families. Since the entries in BigTable have different timestamps, it is possible to understand how data has changed over time.
If cells remain empty in a BigTable database, they will not occupy space. In order to further optimize the storage space, intelligent algorithms are used for the compression and regular compression as well as the rewriting of the data.
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The Cluster Architecture of Google BigTable
Google BigTable is based on a distributed cluster structure. Requests from clients are sent to a big-table node using a front-end server. Several big-table nodes form a big-table cluster.
Each big-table node in a cluster can handle a specified number of requests. To increase the maximum number of concurrent requests and throughput, it is possible to dynamically add a big table node to the cluster. The actual big table tables are stored as table rows in the SSTable format of the Google file system.
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Google BigTable As a Service From The Cloud
In addition to Google’s internal services, external users can also use Google BigTable. Google provides the database system as a service from the cloud under the name “Google Cloud BigTable” to third parties.
Cloud BigTable is globally accessible via the Internet and can be used as a service for storing large amounts of data in the cloud. Based on Google Cloud BigTable, you can serve a wide variety of applications in the big data environment.
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The Uses of Google Cloud BigTable
With Cloud BigTable, applications based on non-structured data can be realized with high data throughput. Possible application scenarios are Artificial Intelligence, Machine Learning, and Business Intelligence Analysis.
For example, the following data types can be stored, retrieved and processed:
- Financial data such as transactions or prices
- Marketing and customer data such as shopping habits or preferences
- Data from the Internet of Things (IoT) such as consumption data or location data
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The Benefits of Google BigTable
Due to its special structure, the Google BigTable database system offers many advantages. Regardless of the size of the database and application, BigTable has low latency and high throughput.
As a result, Google BigTable can be used to store large amounts of data as well as high-throughput processing and analysis. With BigTable, the database scales easily up to sizes of hundreds of petabytes. At the same time, several million processes per second can be processed. Various applications and workflows are fast, reliable and very efficient to use.
When BigTable is used as part of the Google Cloud BigTable service, all data is encrypted during transmission and storage and is unavailable to unauthorized persons. In addition, all access to data with extensive authorization concepts can be individually configured and secured.
If needed, the clusters that provide Google BigTable can be flexibly expanded. Both adding and removing BigTable cluster nodes is possible dynamically without service interruption.
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