Of late, the blockchain technology has emerged as a revolutionary tool spurring acute interest amid the data community. Also known as the ‘distributed ledger’ technology, blockchain provides a way of recording digital transactions in a way that is crafted to be transparent, secure and efficient. The technology is robust and young. In the next few years, it is expected to hit the mainstream and drive commercialization.
No wonder, the blockchain technology is secure and full of positive outcomes yet there exist too much confusion and misunderstanding regarding its essence.
Myth 1: Blockchain is a database full of magical powers
Blockchain is nothing but a simple list of transaction journals – “This list is ‘append only so entries are never deleted, but instead, the file grows indefinitely and must be replicated in every node in the peer-to-peer network”. It doesn’t make room for any sort of physical data storage, like a PDF file or Word document.
Myth 2: Blockchain is the next big change (for good)
Of course, Blockchain is used to perform technical and intricate transactions. It works wonderfully when it comes to mitigating the risk of online fraud, nevertheless, it doesn’t completely eradicate the risks imposed by fraudsters. Thus, it also raises questions on data confidentiality.
Myth 3: Blockchain is free
Although people assume that blockchain is free, the hard fact is that it is neither inexpensive nor highly efficient – YET. It involves several computers to solve myriad mathematical algorithms to formulate a single immutable result, which is eventually known as the Single Version of Truth (SVT).
Myth 4: A single blockchain is in existence
Blockchain is a collective term used for different technologies that are closed or open sourced, available in private or public versions and serve a general-purpose or customized as per needs. However, the common element observed in all of them is that they follow a consensus mechanism and is fleeced up by crypto. Ethereum, Corda, Hyperledger, Bitcoin’s Blockchain and IBM and Microsoft’s Blockchain-as-a-service are all a part of Distributed Ledger Technologies.
Myth 5: Blockchain is the ultimate power technology
Of course, the code is powerful but it’s no magic. Bitcoin and blockchain technologies will definitely lead the future but their authority is limited to mathematics. They won’t replace the job roles of government or lawyers. Cryptocurrency is the fulcrum of blockchain and it’s still far from becoming mainstream.
Myth 6: Blockchain is used only in the financial sector
As a matter of fact, the first application of Blockchain was indeed a bitcoin cryptocurrency, which is a product of the financial sector. Nevertheless, the revolutionary technology has diverse applications across numerous sectors, including finance. Besides finance, blockchain is widely leveraged in healthcare, real estate and FMCG sectors.
At present, Blockchain Technology is evolving at a steadfast rate. Each day, volumes of data records are being created. Such humongous amounts of data need efficient management. For that, the Internet of Things is the key. Dexlab Analytics is a premier Data Science training institute in Gurgaon and we cover a plethora of in-demand skill training courses.
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What is trending in the technical world? Big Data is the word. The sudden upsurge witnessed in the IT Industry has equivalently led to the emergence of Big Data. The complexities of the Data sets are extremely troublesome to co-ordinate activities with the usage of on-hand database management tools. Hence, the shift to this catchy phrase, dealing with homogenous amount of data and is of uttermost importance. Let’s have a quick tete-a-tete with this newest branch of science i.e. Big data Analytics.
- A for A/B Testing– A very essential element of web development and big data industry, it is a powerful evaluation tool to decide which version of an app or a webpage is extremely effective to meet the future business goals. Also, this decision is taken carefully after comparing the numerous versions to select the best from the rest.
- Set the standards for Associate Rule learning– The structure enlists a set of technique in the quest for interesting relationships or the ‘association rules’ amidst variables in massive databases. For better understanding refer to the flowchart attached in the blog, describing a market analysis by a retailer, assuming the products which are high on demand and the usage of this data for successful marketing.
- Get a better understanding of Classification Tree Analysis-In clearer terms, it is the method of recognizing the category in which the new observation falls into. Statistical Classification mainly implements to:
- Classification of organisms into groups.
- Automatically allocating documents into categories.
- Creating profiles of students enrolling for the online courses.
PS: For the better understanding, take a quick glance at the illustration attached below.
- Why would you opt for Data Fusion and Data Integration? The answer is simple. The blending of data from multiple sensors, data integration and fusion leads to the total accuracy and direct more specific inferences which otherwise wouldn’t have been possible from a single sensor alone.
- Mingling with Data Mining – To be precise, Data Mining is nothing but the collective data extraction techniques to be performed on a large chunk of data. The parameters include Association, Classification, Clustering and Forecasting.
- The cloning of Neural Networks- This includes Non-Linear predictive models for pattern recognition and optimization.
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Do you know why several organizations face problems while implementing Big Data? Still wondering? The reason is lack of poor or non-existent data management strategies.
Proper technology systems need to be adopted. Without procedural flows, data is impossible to be analysed or delivered appropriately. However, before we delve deeper into making a plan to introduce data management strategies into the business, we should pay enough attention to the systems and technologies we are thinking to launch, along with the number of improvements to be made.
Big Data is ruling the tech world. Here are few types of tech that needs to be a part of a successful data management strategy:
Common data mining tools are R, SAS and KXEN.
More consistent, Automated ETL is used to extract, transform and load data.
They are efficient in offering a protective layer of security and quality assurance by doing a proper problem diagnosis and monitoring critical environments.
BI and Reporting Analytics
Turn data into insights, with BI and Reporting Analytics. It is very vital that data go to the right people and of course in the right manner. If that doesn’t happen, organizations suffer incessantly.
Analytics is a huge branch of study, starting from customer acquisition data, tracking details to intriguing user-friendly interfaces and product life cycle.
For More Details, Read The Full Blog Here:
Understanding The Core Components of Data Management
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The ‘trending’ topic this season is Data Processing. The statistics attached with this blog depicts that respondents have mainly voted for NoSQL and SQL databases. The opinions of the respondents have conferred the title of ‘most engaging’ to NoSQL database, confirming the second position with a 74.8%.
The survey declares the PostgreSQL as the confirmed winner, where 25.3 % have proclaimed it to be ‘very interesting’ and 37.7 % have confined within ‘interesting’.
Also read: Top Databases of 2017 to Watch Out For
- Elasticsearch declared runner-up with an overall 59%.
- The amalgamation of Lucene and Solr roaring with 43.8%
- More interest devoured in Apache Spark with 3%
- Hadoop scoring a meager of 8%
Next, it unveils that the US respondents have mainly opted for Elasticsearch to PostgreSQL, and Oracle have failed to evoke any interest in the mind of US respondents. However, the picture is completely opposite for the European respondents.
Also read: Data Analytics for the Big Screen
The ending note states that it is high time we realize that the dire need of the hour is data storage and processing. This conclusion is supported by the fact that so many respondents have invested their valuable time in the survey and clearly shows that database is here to stay.