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Big Data Infrastructure

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Last Updated: 02 July 2021

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General | Latest Info

Big data can bring huge benefits to businesses of all sizes. However, as with any business project, proper preparation and planning is essential, especially when IT comes to infrastructure. Until recently, IT was hard for companies to get into big data without making heavy infrastructure investments, but times have change. Cloud computing in particular has opened up a lot of options for using big data, as IT means businesses can tap into big data without having to invest in massive on-site storage and data processing facilities. In order to get going with big data and turn IT into insights and business value, it is likely youll need to make investments in the following key infrastructure elements: data collection, data storage, data analysis, and data visualization / output. Let's look at each area in turn. This is where data arrives at your company. IT includes everything from your sales records, customer database, feedback, social media channels, marketing lists, email archives and any data gleaned from monitoring or measuring aspects of your operations. You may already have data you need, but chances are you need to source some or all of the data require. If you do need to source new data, this may require new infrastructure investments. Infrastructure requirements for capturing data depend on the type or types of data require, but key options might include: sensors; apps which generate user data; CCTV video; beacons; changes to your website that prompt customers to more information; and social media profiles. With little technical knowledge, you can set many of these systems up yourself, or you can partner with a data company to set up systems and capture data on your behalf. Accessing external data sources, such as social media sites, may require little or no infrastructure change on your part, since youre accessing data that someone else is capturing and managing. If youve get a computer and Internet connection, youre pretty much good to go. This is where you keep your data once IT is gathered from your sources. As the volume of data generated and stored by companies has explode, sophisticated but accessible systems and tools have been developed to help with this task. Main storage options include: traditional data warehouse; data lake; distribute / cloud-base storage system; and your company's server or computer hard disk. Regular hard disks are available at very high capacities and for very little cost these days and, if youre small business, this may be all you need. But when you start to deal with storing and analyzing large amounts of data, or if data is going to be a key part of your business going forward, more sophisticated, distributed system like Hadoop may be called for. I think cloud-base storage is a brilliant option for most businesses. Its flexible, you dont need physical systems on-site and IT reduces your data security burden. It is also considerably cheaper than investing in expensive dedicated systems and data warehouses.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Slow storage media

Most Storage providers provide tiered Data Storage that is facilitated by artificial intelligence. This AI takes rules for storing data that you define and automatically apply them to determine where data is store. The primary tier of Data Storage is in-Memory Storage or Solid-State Drives, where your Frequently Access Data is store. Data that is intermittently used can be stored in the Secondary tier of Data that uses less expensive Hard Drive Storage. Data that is seldom used or your cold Storage data, is assigned to very slow disk drives or tapes that are your most inexpensive Storage media. By taking advantage of this automation, you can be sure your heavily Use data is always readily available to users at the same time your seldom used data is stored at least cost.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Lack of scalability

D ata is a vital element of today's innovative enterprise. Data-drive decision making allows corporations to adapt to an unpredictable world. The ability to report on data is the spine of business analytics. With the unprecedented growth of data in the 21st century, big data is no longer a buzzword but a reality that companies have to face. Data expands exponentially and IT requires at all times scalability of data systems. Building a big data pipeline at scale along with integration into existing Analytics ecosystems would become a big challenge for those who are not familiar with either. To build a scalable Big data Analytics pipeline, you must first identify three critical factors: either they are time-series or non-time-series, you must know the nature of your pipelines ' input data. IT would determine under what format you store your data, what you do when data is missing, and what technology you use in the rest of the pipeline. When building an Analytics pipeline, you need to care about end-users. Data Analysts use your pipeline to build reporting dashboard or visualization. Output data needs to be accessible and manipulable, giving end-users possible lack of strong technical expertise in data engineering. Nowadays, famous Analytics engines ease integration between big data ecosystems and Analytics warehouses. The Scalability of your data system can decide the long-term viability of business. Theres nothing much alike between handling 100 GB and 1 TB a day. Hardware and software infrastructure must keep up with sudden changes in data volume. You dont want to overload your data system due to the organic growth of your business. Scale your data pipeline for best! However, Vertica shows some disadvantages of working with real-time data or high-latency Analytics. Its constraints on changing schemas or modifying projections limit its use of data with rapid transformation. Druid is an open-source Analytics database specifically designed for Online Analytics Processing. Time-series data requires optimized storage mechanism and fast aggregators. IT contains mostly timestamps and metrics. Druid stores metrics as columns and partitions data based on indexes and metrics altogether for quick access, therefore, provide agile aggregation operations. Data monitoring is as crucial as other modules in your Big data Analytics pipeline. IT detects data-related issues like latency, missing data, inconsistent dataset. The quality of your data pipeline reflects the integrity of data circulating within your system. These metrics ensure minimum or zero data loss transferring from one place to another without affecting business outcomes. We cannot name all metrics log by data monitoring tools because each data pipeline has its specific needs, hence specific tracking. If you are building a time-series data pipeline, focus on latency-sensitive metrics. If your data comes in batches, make sure you track properly transmission process. Some data monitoring tools can help you to build a straightforward data monitoring dashboard, but to suit your particular uses, it is best to build one yourself.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Slow network connectivity

Ensuring application performance on mobile devices is more difficult than on laptops or PCs. This is due in part to wide variation in devices, carriers and O / S. IT cannot install monitoring agents on employee-own devices due to privacy concerns and, of course, IT cannot install agents on its customers ' smartphones. Instrumenting apps itself enable IT to monitor performance while steering clear of privacy issues. Key metrics to measure include: crashes, errors, Service performance response time, Network, battery, and Signal strength. IT is also important to monitor end-to-end business workflow, such as time to process claim, in order to manage service level agreements and identify problems before users are impact. Developers should instrument internal applications before deploying them in the app store. For third-party apps, many application performance monitoring vendors provide wrappers for instruments mobile apps without tagging code. When slowdown do occur, prudent organizations follow a consistent process to identify the root cause in mobile applications: 1 isolate problems with code, Network, or infrastructure layers, and use code-level stack trace to speed resolution. 2 analyze performance of apps across device and OS versions, geographies, and carriers to identify trends. 3 Track usage, crashes, errors, HTTP performance, and volume relative to thresholds and geography. 4 compares performance of mobile apps across geographies, carriers, devices, and OS versions to optimize performance. 5 trace transactions from user, over the Network, and into the backend. 6 reconstruct incidents to fix issues across data centers, cloud services, and containers / microservices. Troubleshoot problems by monitoring internal and public-facing mobile apps, assessing performance by geographic location and drilling down to analyze key metrics. Fixing slowdowns means IT is in reactive mode. To improve Digital experience, application performance monitoring should provide proactive Insights. By setting performance thresholds at transaction level, operations and DevOps teams can remedy problems before users are impact and proactively enforce SLAs with providers.


Wheres the Problem?

With slow mobile and Cloud applications, sluggishness is often not the root cause, but rather a symptom of underlying infrastructural issues hidden from view. Issues that occur within end user devices, network, Cloud, Web servers, application servers, infrastructure, and apps can all cause mobile application outages or slowdowns. Let's look at each of these potential problems in detail: end-user devices-although closest to customer, device is often the most difficult to diagnose. There are so many variations in devices / carriers that it is hard to pinpoint problem without using end user experience monitoring. Typical problems include: device malfunction, OS failure / OS out of date, and geographic / carrier issues. Network-backhauling network traffic to central data center for security and data protection can also impact performance, especially in Cloud-base and mobile applications. More data-intensive applications such as video and rich media can also slow network. Typical problems include: excessive retransmission, network congestion, network latency, packet loss, and jitter. Cloud services-if you use services from AWS, Microsoft Azure, or Google Cloud, your application could suffer performance slowdowns when any of these underlying services are impact. Compounding the problem is that often only part of the stack has migrated to the Cloud, and IT may be difficult to diagnose if problem is within services that you control. IT can be difficult to monitor performance, ensure consistent user experience, and enforce SLAs. Typical problems include: regional outages / slowdowns and lack of failure strategies. Infrastructure-server problems contribute to many major outages, and infrastructure configuration problems are common in most of these instances. Typical problems include: configuration error, device malfunction, outdated OS, CPU saturation, overcommitted hypervisors, load balancers, and poor performing database queries / overloaded database Web server-Nothing causes problems faster than Web server error. Link or page errors can immediately halt application in its tracks. Typical problems include: missing link, page not find, and internal server error. APIs-Specific APIs will be unique to your application, but some of them to watch include: user authentication / single sign-on, pricing and merchandising, supply chain and logistics, payment gateways and billing systems, and advertising APIs. App itself-Microservices and DevOps practices have accelerated release cycles and introduced greater complexity and inter-dependencies. Typical problems include: bad data call, memory leak, Microservices failure, issues with downstream Web services, authentication error, and code error.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sub-optimal data transformation

Businesses must deliver consistent high-value experienceor risk losing customers to someone who can. And theyre turning to Big Data Technologies to help. With Big Data Analytics, organizations can get to know their customers better, learn their habits and anticipate their needs to deliver a better customer experience. But the path to Big Data Transformation is not a simple one. Legacy Database management and Data Warehouse appliances are becoming too expensive to maintain and scale. Plus, they ca meet today's challenges of accommodating unstructured Data, Internet of Things, streaming Data and other Technologies integral to digital Transformation. The Answer To Big Data Transformation is in the Cloud. Sixty-four percent of IT professionals involved in Big Data decision-making have already Shift technology stack into the Cloud or are expanding their implementation. An ADDITIONAL 23 % are planning to shift to Cloud in the next 12 months, according to research from Forrester. 1 benefits of leveraging Cloud are significant. Advantages most often cited by survey respondents were lower cost of IT; competitive advantage; ability to develop new insights; ability to build new customer applications; ease of Integration; limited Security risks; and reduced Time To value. Big Data Cloud Challenges While the benefits of the Cloud are substantial, shifting Big Data can introduce several challenges, specifically: Integration: 66 % of IT professionals say Data Integration has become more complex in public Cloud Security: 61 % express concerns around data access and storage Legacy: 64 % say Transitioning from Legacy infrastructure / Systems is too complex Skills: 67 % say they are concern about Skills require for Big Data and building require infrastructure 4 Steps To Overcoming Cloud challenge How do You overcome these Challenges and turn them into opportunities? Here are four key Steps to leveraging Cloud for Big Data Transformation: Data Integration If Your Enterprise has a diverse and complex data ecosystem, not all Cloud or Big Data Technologies may be capable of seamless Data Integration. Choosing Target technology that would require complex Data Transformations may not be ideal. Complete Data pipeline analysis before selecting any technology. This will reduce your risk of creating disjoint data and incompatible systems. Security If your data is confidential and proprietary, or you need to address strict security and compliance requirements, you may be concerned about putting your data in Cloud. In this case, single-tenant, private Cloud solution with highly customized network and encryption can give you the data capabilities you need, plus Security of dedicated environment. Also, remember that public Cloud doesnt mean no security. Leading providers such AS Amazon Web Services and Microsoft Azure provide Cloud-native Security authentication Solutions and have options that include disk-level encryption and rigorous authorization and authentication technologies. Data Security on the Cloud is rapidly maturing. Many organizations with stringent security and compliance requirements have successfully leverage Big Data Technologies on public Cloud. Legacy Systems Transitioning from Legacy infrastructure always involves Data migration and usually involves one of three paths: 1.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Hadoop

To recap, Hadoop is essentially an open-source framework for processing, storing and analysing data. The fundamental principle behind Hadoop is rather than tackling one monolithic block of data all in one go, it is more efficient to break up & distribute data into many parts, allowing processing and analysing of different parts concurrently. When hearing Hadoop discuss, it is easy to think of Hadoop as one vast entity; this is a myth. In reality, Hadoop is a whole ecosystem of different products, largely presided over by Apache Software Foundation. Some key components include: HDFS-The default storage layer MapReduce-Executes wide range of analytic functions by analysing datasets in parallel before reducing results. Map job distributes queries to different nodes, and reduces gathers results and resolves them into single value. YARN-Responsible for cluster management and scheduling user applications Spark-use on top of HDFS, and promises speed up to 100 times faster than the two-step MapReduce function in certain applications. Allow data to load in-memory and query repeatedly, making it particularly apt for machine learning algorithms. More information about Apache Hadoop add-on components, can be found here. The main advantages of Hadoop are its cost-and time-effectiveness. Cost, because as its open source, its free and available for anyone to use, and can run off cheap commodity hardware. Time, because it processes multiple parts of the data set concurrently, making it a comparatively fast tool for retrospective, in-depth analysis. However, open source has its drawbacks. Apache Software Foundation are constantly updating and developing the Hadoop ecosystem; but if you hit a snag with open-source technology, there is no one go-to source for troubleshooting. This is where Hadoop-on-Premium packages enter the picture. Hadoop-on-Premium services such as Cloudera, Hortonworks and Splice offer the Hadoop framework with greater security and support, with added System & data management tools and enterprise capabilities.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

NoSQL

Big data is getting bigger and more chaotic every day. Thanks to the Internet, social media, mobile devices and other technologies, massive volumes of varied and unstructured datastreaming at unprecedented speedsare bombarding today's businesses both large and small. This explosion of data is proving to be too large and too complex for relational databases to handle on their own. Fortunately for organizations, new breed of Database has rise to Big Data challengethe not only SQL Database. Up until recently, relational Databases such as Oracle, Microsoft SQL Server, and MySQL enjoyed monopoly. But that is rapidly changing. In the last 5 years, NOSQL Databases such as MongoDB and Apache Cassandra and HBase have enjoyed exponential growth in comparison to their RDBMS counterparts. This stratospheric rise in adoption of NOSQL does not suggest that the demise of traditional data warehouses is on the horizon. However, it does show that many organizations are turning to NOSQL as a more Cloud-friendly solution to their Big Data problems. If your Organization is ready to do more with Big Data, this comparative look at NOSQL and RDBMS to help you better decide if NOSQL is right for you.


Scalability

Typical RDBMS scales vertically due to monolithic architecture. This means that a single server must be made increasingly more powerful in order to accommodate increasing data demands. As data needs increase, more physical servers must be added to the cluster. And while spreading RDBMS over many servers is possible, it is a costly, time-consuming process that usually requires extra engineering. NoSQL databases offer efficient architecture that scale-out horizontally. This means that increasing storage and compute capacity is merely a matter of adding more commodity servers or cloud instances. In addition, open-source nature of NoSQL makes it much more cost-effective than traditional relational database.S

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Massively Parallel Processing (MPP)

Big Data is a term that describes the large volume of data that inundates businesses on a day-to-day basis. Algorithms that work well on small datasets crumble when the size of data extends into terabytes. Organizations large and small are forced to grapple with the problems of Big Data, which challenges existing tenets of Data science and Computing technologies. The importance of Big Data doesnt revolve around how much data you have, but what you do with it. In the early 2000s, Big Data storage problems were solved by companies like Teradata that offer unified architecture able to store petabytes of data. Teradata CAN seamlessly distribute datasets on multiple Access Module Processors and facilitates faster analytics. Teradata Database is a highly scalable RDBMS produced by Teradata Corporation. It is widely used to manage large data warehousing operations with Massive Parallel Processing. It acts as a single Data store that accepts many Concurrent requests and complex Online Analytical Processing from multiple Client applications. Teradata has patented software Parallel Database Extension which is instal on hardware component, This PDE divides processor of system into multiple virtual software processors where each virtual processor acts as an individual processor and it CAN perform all tasks independently. In similar fashion, hardware disk component of Teradata is also divided into multiple virtual disks corresponding to each virtual processor. Hence, Teradata is called share-nothing architecture. Teradata uses Parallel Processing, and the most important aspect of this is to spread rows of table equally among AMPs who read and write data. It uses a hashing algorithm to determine which AMP is responsible for data row storage and retrieval. It will generate a 32-bit hash value whenever the same data value is passed into it. Teradata Studio-Client base graphical interface for performing Database Administration and Query Development. Teradata Parallel Transporter-Parallel and scalable Data-loading and unloading utility to / from external sources. Viewpoint-provide Teradata customers with Single Operational View-System Management and monitoring across Enterprise for both administrators and business users. Row-level security-allow restricting data access on a row-by-row basis in accordance with site security policies. Workload Management-workload is a class of Database requests with common traits whose access to Database CAN be managed with a set of rules. Workload Management is act of managing Teradata Database workload performance by monitoring System activity and acting when pre-define limits are reach. Teradata Connector for Hadoop-BI-directional Data movement utility between Hadoop and Teradata which runs as MapReduce application inside the Hadoop cluster. QueryGrid-Teradata-Hadoop Connector provides a SQL interface for transferring Data between Teradata Database and remote Hadoop hosts. IntelliCloud-Secure cloud offering that provides data and analytic software as service. It enables Enterprise to focus on Data warehousing and analytic workloads and rely on Teradata for setup, Management, maintenance, and support of software and Infrastructure-either in Teradata Data centers or using public cloud from Amazon Web Services and Microsoft Azure. One famous automobile company uses Teradata for product development process.


What is an Analytical MPP database?

MPP databases are very good for most common analytical workloads, which are generally characterized by queries on subset of columns with aggregations over broad ranges of rows. This is due to their columnar architecture, which allows them to only access fields needed to complete query. Columnar architecture also gives MPP databases additional features that are useful for analytic workloads. These vary by database, but often include the ability to compress like data values, efficiently index very large tables, and handle wide, denormalized tables. Organizations typically use analytical MPP databases as data warehouses, or centralized repositories that house all data generated within their organization, such as transactional sales data, web tracking data, marketing data, customer service data, inventory / logistical data, HR / recruiting data, and system log data. Because analytical MPP databases can handle huge data volumes, organizations can comfortably rely on these databases to not only store data, but also support analytical workloads from these various business functions. Analytical MPP databases can easily scale their compute and storage capabilities linearly by adding more servers to the system. This opposite of vertically scaling compute and storage capabilities, which involve upgrading to larger and more powerful individual servers, and which generally hit wall at scale. Analytical MPP databases are able to scale out so quickly, easily, and efficiently that on-demand database vendors have automated that process to scale the system up or down depending on the size of query.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Big data: threat or savior?

The Evolving growth of Enterprise World is putting organizations at risk of Cyber-attacks that have increased tremendously over years. Research says rapid digitization will increase the cost of data breaches to 21 trillion by the end of 2019. Today, hackers & cybercriminals are constantly creating bigger threats leading to lack of data security, which in turn causes great financial losses and bad reputation for company. As we are all aware of how important business data is for any organization, IT has become the utmost need to understand how to protect information. Analytics is a key element that has the power to leverage Cyber resilience. With the help of effective Big Data Analytics Solutions, companies can deal with growing cyber threats associated with the increased amount of data generated every day. Big Data as Savior or Threat? No matter what business you are dealing with, keeping your data secure and protecting against malware should be the most important task. Many big companies today are facing difficulty sustaining business growth and performance with the rise of never-ending security threats. Will Big Data Analytics help in safeguarding our data? Some say Big Data is a Threat while others believe Big Data is a Savior. With the potential to store large amounts of data, Big Data can help analysts review, examine, as well as detect irregularities inside the network. Information retrieved from Big Data-enable organizations to reduce the time need to detect and resolve issue, as analysts can easily predict & avoid possibilities of Cyber-attacks. Research says more than 80 % of businesses use Big Data to block such attacks and those who were using Data Analytics for their business operations witness a tremendous decline in security breaches. However, due to the overwhelming amount of data, it is possible that the power of Analytics cannot be utilized completely. IT is important that the analytics tools you use are backed by intelligent Risk insights to make IT easy for data experts to interpret data. Avail Cyber Security Audit Services If you want to protect your Enterprise Data from the increasing number of Cyber-attacks and stay update with advances in Big Data Analytics. Role of Analytics in Cyber Security If you are wondering how Big Data and Cyber Security are relate, we have an answer for you! Technological innovation has taken the world by storm and IT has become a necessity for organizations to use Big Data Analytics & perform deep analysis of information. This will give idea about potential threats that may hamper the integrity of a company. As per information extract, organizations can create baselines based on statistical data to highlight any difference from normal processes. After finding out deviation from the norm with Data collect, business owners can work on new strategies to enhance business goals better. Using advanced technologies like Artificial Intelligence and Machine Learning, new predictive models can be made based on statistical data.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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