Big Data Made Simple - One source. Many perspectives.

Let’s assume you run a business enterprise and it has major data sources that generate real-time information about the users. Choosing a right big data tool for your enterprise is an important step because once you begin with the project, it is extremely cumbersome and resource-intensive to shift from one solution to another. In today’s post, we have compiled the top 5 big data tools, along with their list of significant features, which can be used by an enterprise to computationally analysis the data and reveal clever insights. Let’s have a look.

1. Apache Hadoop

Apache Hadoop is one of the most prevalent big data tools for accurately analyzing large data volumes. It is basically an open-source, Java-based programming framework that allows for the processing and storage of large datasets with the help of simple programming models. Hadoop is extensively being used for many big data processing jobs, including statistical analysis, sales planning as well as processing the colossal sums of data generated by the IoT sensors.

Benefits

• Hadoop is an open-source framework which runs on low-cost commodity hardware.
• It deploys MapReduce programming model that enables it to process, manage and store data at a petabyte scale, where petabytes of data can be processed in a couple of hours.
• Large computing clusters are often prone to failures. However, Hadoop is highly reliable. For instance, if a node in the cluster fails, the data processing is automatically re-directed to the remaining nodes and the data is re-replicated in order to combat any future node failures.
• With Hadoop, you can store your data in any format, may it be structured or unstructured. And later, when the data has to be read, you can apply any structured schemas to it.

2. Cassandra

Apache Cassandra is a distributed NoSQL database management system that allows managing large volumes of data located across a number of commodity servers. This is a highly effective tool that manages several nodes simultaneously, leaving no single point of failure. Due to its high availability, ease of operation, hassle-free data distribution and a great ability to scale, some of the biggest data enterprises such as Instagram, eBay, GoDaddy, Netflix, and Apple use Cassandra for executing real-time analytics.

Benefits

• Cassandra is an open source framework which comes with an extensive community where a number of people share their views related to Big Data. Also, Cassandra can be easily integrated with Apache’s other projects such as Hadoop, Apache Hive, etc.
• In Cassandra, the data is automatically duplicated to multiple nodes to avoid failures. Also, failed nodes can be replaced with new nodes without having to take it down, thus no downtime.
• It is decentralized. Every node in the computing cluster is identical. Thus, there is no single point of failure.
• Cassandra is suitable for enterprises that cannot afford to lose a single piece of data, even in case of failure of the entire data center.
• Cassandra comes with a rich data model which is largely column oriented. All the data is stored in columns, unlike traditional databases that make the storage effectual and well-organized.

3. Elasticsearch

The best part about elastic search is that it is highly flexible, i.e. it allows the user to obtain data from any source, in any format and in any quantity and analysis it to reveal valuable insights. It is horizontally scalable and is known for its reliability and ease of management.

Benefits

• In contrast to traditional databases that usually take more than 10 seconds to fetch results, Elasticsearch returns results in under 10 milliseconds on the same hardware.
• Combining the speed of search with the potential of detailed analysis, Elasticsearch observes a developer-friendly JSON style query language that works perfectly for both structured and unstructured data.
• Elasticsearch features a distributed architecture. It can scale up to hundreds of servers and store petabytes of data.

4. KNIME

KNIME, also known as Konstanz Information Miner, is an open source data analytics tool that allows you to discover the amazing potential hidden in your data and source clever insights. Easy to deploy and scale, it incorporates various components for machine learning and data analyzing through its modular data pipelining concept.

Benefits

• Every node in the clusters stores all the data permanently. Therefore, the workflow in the execution can be stopped at any node and easily resumed later on.
• KNIME allows additional plugins that enable the integration of various methods for text mining and image mining.

Final words

Big Data holds immense potential in terms of deriving real-time acumens about your user’s behavior. It can help you propel your business in the right direction by obtaining significant information about your user needs and preferences. These afore-mentioned open source Big Data tools can certainly help you remove the difficulty of managing colossal volumes of data. However, you need to understand these in detail so as to know the right fit for your business analytic needs.

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Source: http://bigdata-madesimple.com/top-open-source-big-data-tools-for-your-business-analytic-needs/

The blockchain is disrupting various areas of society and it is poised to provide solutions to challenges that we face today. From maintaining health records to real estate, the technology is making a social impact. International brands already investing in this technology, including IBM and Walmart. But these huge companies aren’t the only ones that recognize the technology’s potential. Some startups are embracing it to disrupt all sectors that deal with various data and transactions.

The technology isn’t new. It’s been introduced in 2008. Today, however, blockchain startups are building their platforms around this technology. Some of the companies crowdsource the investments using ICOs. The technology provides new possibilities for new companies in various industries. The interconnectedness among the startups will lead to a diverse ecosystem.

These top blockchain startups are based in the USA and built on the blockchain technology. What are these blockchain startups in the USA that you should look after?

Rentberry

This blockchain startup is a rental platform for tenants and landlords. It was founded in 2017 and facilitate the process of renting, makes it more convenient, secure and transparent. It is also cheaper because it eliminates the need to hire agents and brokers. It provides a network to crowdsource security deposit and establish a network that unfreezes security deposit funds. You can use the platform to screen tenants, pay rent, negotiate rental fees and e-sign contracts. Landlords and tenants can make an educated decision about pricing.

Rentberry has an option of using BERRY token for any transactions. All transactions are stored securely on the blockchain making them more secure than the conventional way of saving data.

Ripple

It is a blockchain startup based in San Francisco that offers real-time payment system. It allows banks to transact with one another without the use of a central correspondent. You can also use it as a real-time gross settlement system.

It offers a solution that enables companies to access cross-currency liquidity using a distributed network. This process allows the foreign exchange to be sourced from the competitive marketplace or trading desk. Through it, companies can minimizes foreign exchange exposure, and as a result, lower the volatility and the risk of trades.

The platform features insider perspectives, updates, and comprehensive market analysis. It is also considered as the most scalable digital asset that allows real-time global payments.

Shopin

It mimics amazon.com’s model. It brings online retailers to form a so-called distributed Amazon. A shopper will have a universal profile that can be used to view shopping history from participating retailers. The shoppers will also receive product recommendations.

TraDove

It is a version of LinkedIn for businesses that stored on the blockchain. This startup is the first company that focuses on B2B solutions and tokens. It utilizes smart contracts to customize the international trade. It replaces tedious and expensive payment methods. It also implements big data and AI solutions.

Coinbase

To trade your digital currencies, you need a platform. You also need to have an intermediary so that you can communicate with the network. This is where Coinbase comes in.

It is a secure online platform that lets you sell, transfer and buy digital currencies. It is an open financial system for individuals to convert digital currency to local currency and vice versa. It also makes buying and selling any digital currencies a lot easier. Plus, you can use this service to send and receive cryptocurrencies from your friends.

Bittrex

It is one of the most popular crypto exchanges that offer a great number of trading pairs to bitcoin. It operates under strict security protocols and uses two-factor authentication system.

Gem

This blockchain platform bridges companies to share valuable information. It offers decentralized blockchain for information that can be replicated across various companies. The company currently targets health care and supply companies. It is also working on integration with auto-insurance industry.

Digital Asset

It provides blockchain solutions to financial institutions. JPMorgan, Accenture, IBM, and Citi are just some of the companies that invested in this service. Digital Asset’s platform is designed to cater to financial institutions around the world.

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Over the last five years, big data has become one of the most valuable assets in business. Although the process of gathering and storing large quantities of digital information has been around since the nineties, it’s only in recent years that it’s been put to good use. Indeed, as Harvard Business Review’s Kristian Hammond noted in 2013, big data is the means to evidence-based decision-making. By analyzing large quantities of information from a variety of sources, companies can make better decisions and, in turn, grow in a more efficient way. Taking this concept, companies of all sizes and persuasions have developed technology to harness the power of big data.

As per a 2016 report by Forrester and TechRadar, the industry is evolving rapidly. The report noted the trend trajectory of 22 technologies within the big data sector, many of which have flourished as predicted. Of those highlighted, Forbes Gil Press noted 10 of the most significant for businesses:

Predictive analytics

This is the process of using data mining, statistics and modelling to make predictions about future outcomes. In other words, historical data defines a set of parameters, which computers can then use to determine what user behaviour/responses might be in the future.

Search and knowledge discovery

These tools support self-service extraction of information from large, unstructured databases. In other words, search and knowledge tools allow someone to input a query and pull in data from a variety of unconnected sources in order to cover a single topic/request.

Stream analytics

This technology can take information generated from a variety of connected devices and sensors and turn it into actionable insights in real-time. This technology is most concerned with IoT and using data from a variety of smart devices to make almost instant predictions. For example, stream analytics could be used in an IoT home system to help determine the optimal living environment (heat, lighting etc.) for the user based on masses of data from the thousands of homes.

NoSQL databases

The use of NoSQL databases makes the processing of some big data sets more efficient. Because these databases are structured using key-values, graphs or documents instead of tabular structures found in relational databases.

Distributed file stores

These systems store data on multiple nodes instead of a single point. The data is replicated on each node to allow for improved processing performance i.e. the information is more readily available. This type of data storage is similar to the decentralized structure of blockchains.

In-memory data fabrics

This technology groups independent sources of data into a grid. This grouping not only allows each source to operate independently but as part of a collective, through which information can be analyzed either in parts or as a whole unite.

Data virtualization

Drawing information from disparate sources, this technique allows users to gain an overview of large sets of data in real time. This is possible because the software doesn’t replicate the data from each source. Instead, it simply delivers a unified data service that can support multiple applications and users.

Data preparation

With big data insights increasing, it’s becoming increasingly difficult to process it all in an efficient way even with all the current software on the market. Data preparation involves collecting and editing data from multiple sources before it’s plugged into a system and analyzed.

Data integration

To improve the communication between unconnected data sources, integration software has become important. Through products such as Apache Pig and MongoDB, it’s now possible to link data in a meaningful way even if the sources are completely unconnected.

Data quality

Not all data is good data. With speed and efficiency crucial in today’s world, businesses are now using products that analyze and cleanse data before it’s stored/analyzed.

Predictive Analytics starts to shine

Of the innovations listed, predictive analytics is one that’s showing it has the most utility in the current business climate. Despite the fact it’s been around for more than a decade, machine learning (a part of the artificial intelligence realm) has made this technology more effective. Prior to machine learning giving computers the ability to adapt in real-time, predictive analytics struggled with scale. Because AI systems can operate without human intervention, they can process more information. As an example, Magnetic’s AI system can process 1 petabyte of consumer information to suggest potentially profitable actions. Combining this technology with predictive algorithms can result in models that consider more information and, in turn, generate more precise outputs.

In simple terms, predictive models allow businesses to determine customer responses or potential purchases by using historical data. An example of this would be the way gaming operators draw data from in-house marketing campaigns and external comparison sites to define their next marketing campaign. For instance, after sending out a promotional email, the company has the ability to record the number of clickthrough responses. On top of this, affiliate data from comparison sites gives the company further insight into what’s hot and what’s not. Indeed, because a platform like Casinos Killer ranks sites using a myriad of data, including betting options, bonuses and overall quality, it’s easy to see which offers players are attracted to. Just like PriceGrabber and NexTag are hotbeds for user preferences, the same is true in the gaming sector. So, by tracking data from affiliates and combining it with its own insights, it becomes possible to use predictive analytics and AI to highlight trends and launch campaigns based on this analysis.

Of all the trends in big data, the evolution of AI-powered technology is by far the most significant. As we’ve shown, the ability to process larger quantities of data in real-time can result in more accurate predictions. The upshot of this is that business can be more efficient in whatever task it is they’re interested in. Whether it’s security or marketing, the crossovers between big data technology and AI are playing a central role in the action.

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We recently interviewed Nicole Nguyen, Head of APAC, Infinity Blockchain Ventures, who spearheads Infinity Blockchain Lab’s regional initiative in connecting major players and fostering an ever-growing blockchain ecosystem in Asia. She is a co-author of the first Vietnam Blockchain Landscape Report and speaker at various international and local blockchain conferences such as TEDx, Shanghai Blockchain Week, World Bank blockchain workshop series, Seamless Asia and ConnecTech Asia.

Nicole Nguyen will be speaking on the panel ‘Fireside Chat: 10 Key Challenges to Blockchain Adoption’ at ConnecTechAsia Summit on Day 2, 27 June 2018. She shared her views on the new trends, challenges and myth about blockchain technology today.

Read the complete interview below:

1. Can you give us a brief introduction about what this technology is and what is the principle on which blockchain technology is based on?

Blockchain, at the end of the day, is a database. Yet what brings magic to blockchain is the underlying cryptography, consensus algorithms, decentralized database and network. Technically blockchain is not something new – its inception dates back to the 90s yet as a trust-centered technology, blockchain is drastically transforming the way we exchange data and value. By changing the fundamentals of a society – trust and value, blockchain is deemed as a game-changer to the global innovation landscape.

2. Are there different types of Blockchains? What are they?

By definition there are various types of blockchain. To my understanding, basically there are permissioned, permissionless and hybrid blockchains. In permissioned blockchain, the system owner can designate transaction validators and have the authority to remove or alter the data; also the data is only accessible to pre-selected audience. However this is not possible in a permissionless blockchain – arguably claimed as the most democratic blockchain. Hybrid blockchain allows a mixed model where pre-selected nodes or players can validate/approve the data and the public is allowed to see the data yet have no authority to rewrite the data.

Subject to different business requirements in terms of system robustness, privacy, throughput etc, you can consider the most relevant blockchain and most importantly how blockchain could fill the unmet demand in the market like no other technologies can.

3. How will blockchain reshape the future, maybe in next five years? Who are going to be the biggest beneficiaries of blockchain technology and why?

The blockchain is transforming the way people exchange information and value like never before. This is seriously disrupting value and growth drivers of the society and ushering new business models and opportunities. At the end of the day, end users will be the biggest beneficiaries of blockchain in my opinion, yet startups and disruption incumbents such as mainstream players and businesses can find their own way of leveraging blockchain technology to bring about higher efficiency, productivity and sustainable growth to the business.

4. What are the key challenges that the technology needs to overcome today before mainstream adoption?

The blockchain is still at its infancy in terms of awareness and development and challenges still amass such as scalability, lack of regulation, on-boarding process etc. I personally feel there is a shortage of good blockchain products at infrastructure level (payment, exchange, identity etc.) with easy on boarding process and user-friendly interface. Simply as it might sound, I think this is one of the key challenges to blockchain adoption.

5. There is a lot of myths about blockchain right now. Can you name a few and debunk them?

Myths include the following – Blockchain is cryptocurrency. Blockchain brings money from the sky. The blockchain is the trend – you should apply it at all costs. There are plenty of them.

I think my key takeaway is behind the hype and flashy covers, blockchain is all about being relevant when it comes to application in businesses. Once you want to apply blockchain please make sure you have a sharp problem statement and blockchain is the only technology that can fill the missing piece.

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SQL Server and SQL databases are here for quite a while, and there are many techniques and strategies available for the administrators to perform better in it. Many DBAs will not be aware of some of the top tips which will help you mainly in a production environment. Here, we will discuss some random tips which some of you may find informative and helpful in the job of an SQL Server production DBA.

#1. Forfiles utility to get rid of old backup files

Forfiles (forfiles.exe) utility comes pre-installed with Windows Server 2003. IT enables the administrators to perform batch file processing. So, DBAs can use this service in conjunction with the SQL Server Agent, which help delete the old database backups, which will eradicate dependencies on SQL Server maintenance, xp_cmdshell extended stored procedures or the VBScript objects.

#2. Using ‘ALTER USER’ to repair orphaned logins

From SQL Server 2005 SP2, the ALTER USER command of T-SQL’s is having a WITH LOGIN clause too. Choosing this can repair the orphaned logins by changing the user’s SID to security identifier of server login. It can improve not only SQL Server but Windows logins too. Orphaned users get created when a database gets restored from a different server, and that login is independently produced.

#3. Use the ‘sp_addsrvrolemember’ to take up sysadmin role

From SQL Server 2008, sysadmin role is not given to Windows Administrators by default. To cover this, you can initiate SQL Server instance in the single-user mode or maintenance mode and then run ‘sp_addsrvrolemember’ in the Sqlcmd utility stored procedure to add your login to the sysadmin role.

#4. Using PortQryUI for troubleshooting connectivity issues

As suggested by RemoteDBA.com experts, you can use the Microsoft’s PortQryUI to troubleshoot the TCP/IP connectivity issues. PortQryUI is an alternative to PortQry but has a GUI with many predefined services. One of these predefined ports group is meant for SQL Server, consisting of a TCP port 1433 and UDP port 1434. To check the ports, you can just enter IP address or the FQDN (Fully Qualified Domain Name) or target SQL. It is easy to download PortQryUI utility at “PortQryUI.”

#5. Try and use a unique strategy while running the DBCC CHECKDB against bigger databases.

Databases tend to become larger day by, so the maintenance procedures like checking integrity with the DBCC CHECKDB command of T-SQL’s may take longer and longer. There are several potential solutions if the DBCC CHECKDB exceeds allocated maintenance period. One instant solution is to access backup to restore DB on a different server and then run the DBCC CHECKDB against the existing server. A second solution is to custom set database’s verify option to CHECKSUM and then run DBCC CHECKDB with PHYSICAL_ONLY option. This combination will make the DBCC CHECKDB run in lesser time, but will still catch the I/O subsystem.

You can also use the T-SQL’s Server 2008 Center Management servers and local server groups to query multiple servers simultaneously. In an SQL Server 2008, you can also use local server group to connect to the servers frequently accessed quickly.

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Source: http://bigdata-madesimple.com/5-quick-tips-for-sql-server-production-dbas/

Artificial Intelligence is the latest buzzword in marketing and it’s hard to live a day without hearing it at least half a dozen times. But what really is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of computer science that theorizes and develops computer systems that can perform tasks that usually require human intelligence. These include but are not limited to speech recognition, visual perception and decision-making.

Research has shown that the possibilities for use and leveraging of AI are endless and it can serve a variety of functions in your business. As much of marketing is currently focused on digital, it is no wonder that AI has come to take a firm place in the field. As a result of this, AI can be used to improve your business or blog. Let’s take a look at some ways that AI has been used recently to increase traffic to a website or blog.

5 ways AI can be used to improve your business

Content Creation – Content marketing is currently one of the best strategies for many marketers and bloggers. AI allows you to access information on how persons consume and interact with the content on your website or blog. According to Jeff O’Brien, Head of Content Marketing at SolidEssay, it will help you to gather information and resources needed to create new content and identify trends that can help you make critical decisions for your blog.

Search Engine Optimization – In order for your website or blog to gain new visits, you have to be found. AI can be used, along with search engine optimization tools, to identify issues on your blog or website and suggest areas and ways for improving your search ranking.

Website creation – AI makes website creation easier and better by providing a fully optimized website or blog as the end result.

Real-time personalization – AI helps you to provide personalized messages, posts, recommendations, etc. for your blog or website, thus making your audience happy and more likely to continuously read your content.

Accessibility – AI can help to make your blog or website more accessible to persons who are visually-impaired or hearing-impaired. You can use AI-based tools to transcribe video, provide closed captioning or audio for your blog.

Your blog cannot survive without readers, so you have to employ several strategies to bring readers to your blog and to keep them coming back. Now that you know what AI is and a few ways it can be used for your blog or website, let’s look at some great AI tools you can use to grow your blog so you not only gain new readers but also keep the established ones coming back for more.

5 AI tools to grow your blog

Grammarly – Well-written blog posts grab their audience’s attention and make them crave more. In order to write a great blog post, you need a great writing tool. Grammarly is just that. It uses AI to identify grammar and spelling errors while also giving suggestions on how to improve the tone and style of your writing. No writer is complete without a tool like Grammarly.

Crayon – AI is used to track what the competitor is doing. It analyzes data from online sources, tracks the activity of your competitors’ websites and provides useful insight for your business. These can be used to help make meaningful business decisions about what to write, how to post and so much more. Be sure to try this tool for your own blog and see how much a difference it makes.

Uberflip – This is a great tool for analyzing your written content and cataloging it. It then uses this to recommend specific content to your audience based on posts they have read in the past. It is a great way to keep your readers engaged and coming back for more.

Pathmatics – Do you know how to find your audience online? Knowing this will better enable you to find and target them. If you don’t, try using Pathmatics. It uses AI to provide you with digital advertising information from brands, companies, etc. Use it to identify your competitors and keep track what they are doing, how they are spending their time, etc. It will also tell you how the audience is responding to the ads, thus enabling you to make informed decisions about your own advertising of your blog. Amanda Daniels, Chief Marketing Officer at ConfidentWriters, recommends using the information to identify companies or brands that you can align yourself with to get more readers to your blog or even guest blog on their platform. The possibilities for this are endless!

BrightEgde – This tool makes content production easier because it helps with tasks such as adding header tags, cross-linking and optimization. It tells you what types of content perform better and suggests ways to rank your written content so that your readers are continuously engaged. Measuring your ROI for your blogging has been made easy because of BrightEdge, so be sure to give it a try.

Blogging is a way to expand your knowledge and to grow, whether personally or professionally. Whether you do it as a hobby, side job or main job, your blog can only survive if it has readers and if it grows. The tools given are great, easy to use tools that will help you do just that. Be sure to give them a try.

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