In the realm of digital innovation, big data has emerged as a cornerstone, empowering businesses to glean invaluable insights from vast pools of information. However, amidst the proliferation of data, ensuring its accuracy, reliability, and performance becomes paramount. This is where the significance of a robust big data testing strategy comes into play. In this article, we delve into the intricacies of big data testing, uncovering essential techniques and best practices to ensure seamless data operations and optimal performance.

Big data testing encompasses a set of methodologies and processes aimed at validating the quality, integrity, and usability of large datasets. Unlike traditional testing approaches, which primarily focus on functional aspects, big data testing extends its scope to encompass various dimensions, including volume, velocity, variety, and veracity. By meticulously scrutinizing these facets, organizations can mitigate risks, enhance data quality, and unlock the true potential of their data assets.

Despite its transformative potential, big data testing presents several unique challenges that warrant careful consideration. These challenges include:

1. Scalability: As data volumes continue to escalate exponentially, traditional testing frameworks struggle to accommodate the sheer scale of big data environments.

2. Diversity of Data Sources: Big data ecosystems often comprise a diverse array of data sources, ranging from structured databases to unstructured sources like social media feeds and sensor data.

3. Real-time Processing: With the proliferation of real-time analytics, organizations must ensure that their testing methodologies can effectively validate data streams in a timely manner.

4. Data Quality Assurance: Maintaining data integrity and quality is paramount, necessitating robust validation techniques to identify anomalies, inconsistencies, and inaccuracies.

5. Performance Optimization: Big data applications must deliver high-performance outcomes, necessitating performance testing to evaluate response times, throughput, and scalability under varying workloads.

To surmount these challenges and ensure the efficacy of big data initiatives, organizations must adopt a comprehensive testing strategy encompassing the following best practices:

1. Define Clear Testing Objectives: Establish clear testing objectives aligned with business goals, delineating the scope, success criteria, and key performance indicators (KPIs) for the testing process.

2. Embrace Automation: Leverage automation tools and frameworks to streamline testing workflows, enhance efficiency, and accelerate time-to-market for big data applications.

3. Implement Data Generation Techniques: Employ synthetic data generation techniques to create diverse datasets representative of real-world scenarios, enabling comprehensive testing across varied use cases.

4. Harness Parallel Processing: Embrace parallel processing techniques to expedite test execution and optimize resource utilization in distributed big data environments.

5. Emphasize Data Validation: Prioritize data validation methodologies, including schema validation, data profiling, and anomaly detection, to uphold data integrity and quality throughout the testing lifecycle.

6. Conduct Performance Testing: Perform rigorous performance testing to assess the scalability, responsiveness, and throughput of big data applications under different load conditions, ensuring optimal performance across diverse scenarios.

7. Foster Collaboration: Promote cross-functional collaboration between development, testing, and operations teams to foster synergy, share insights, and address testing challenges effectively.

Conclusion: Elevating Big Data Testing to New Heights

In the era of digital transformation, big data represents a goldmine of opportunities for organizations seeking to gain a competitive edge. However, realizing the full potential of big data hinges upon the efficacy of testing methodologies deployed to validate and ensure its integrity, reliability, and performance. By embracing a comprehensive testing strategy encompassing automation, performance optimization, and data validation, organizations can navigate the complexities of big data ecosystems with confidence, unlocking actionable insights and driving innovation at scale.

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Data analytics is the process of analyzing poised data in order to help companies make knowledgable business decisions. For that, organizations analyze the data in order to help divulge hidden values. Just as critical, they also make endorsement for how to amend business performance. Data analytic organizations can analyze inventory data advice manufacturers exceptional plan when to purchase parts, authorize how long those parts should be stored, and identify where absolutely to house the inventory – thus improving business operations and performance.

As more organizations implement big data strategies, data analytic companies advice these organizations identify performance issues and make informed business decisions to enhance and optimize results. Those that decline to weight the information in their data, or do it ailing, will suffer cutthroat burden and objections. It is imperative for organizations to invest in and invigorate their data analytic competence.

Data Analytic Process

Data analytics companies employ some form of the following for conducting data analyses strategies and activities:

1. Business requirements need to be carefully inferred with a plan designed for apprehending and safely securing the suited data

2. Business data needs to be collected, cleansed, and organized in a demeanor so that the data can be analyzed

3. Business data is analyzed, outcomes rendered, and intuition provided which may or may not cover visualizations

Data analytics companies have implemented competence to back specific types of business locale, counting information management, machine learning models, and the processing of personal data as origin of information. Data analytics companies often develop analytics effectiveness that are designed to acquire user action. They can also apply capabilities to help automate business processes, provide product support, and provide customer service.

Three different models of Data Analytics

In order for data analytic companies to convey key insights, data professionals utilize any of three types of analytics. These are descriptive analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics: Descriptive analytics assesses historical data to better understand changes that have occurred in a business. The purpose of descriptive analytics is to answer the question “what has happened?”. This is the simplest form of analytics and many companies employ descriptive analytics in their organization.

Predictive analytics: Predictive analytics makes predictions about future outcomes based on historical data combined with analytical techniques. It is an integrated, interactive analytics program aimed at improving the performance and efficiency of its operations in the future. The purpose of predictive analytics is to answer the question “what may happen?” Predictive analytics is a forecasting technique.

Prescriptive analytics: Prescriptive analytics focuses on identifying the best course of action in a scenario given the available data. It also suggests courses of actions that depend on the results of descriptive and predictive analytics. Optimization scenarios are assessed to answer “what should we do?”.

Platforms and Tools

Some of the more famous tools utilized by data professionals and data analytics companies include the following:

Apache Spark: Apache Spark is an open source big data platform which combines data science with web analytics to provide a high-quality, data driven, and accurate analytics solution to applications.

Python: Python is an open source scripting language which is easy to learn and has a plethora of libraries, many of which are useful to data professional.

R Programming: R is an open source programming tool primarily used for statistics and data modeling provided by the R Foundation for Statistical Computing.

Power BI / Tableau: Power BI & Tableau are interactive data visualization software tool used by data analysts, data scientists, and others to present data in a meaningful and interactive way. Both tools have a free version as well as paid version of their visualization tool.

SAS: SAS is an analytics platform offered by the SAS Institute which helps organizations access, manage, analyze and report on data to aid in decision-making.

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Big Data is getting bigger every day – be it the investment, popularity, dependency or growth. Within a significantly short period of time, big data evolved from being a ‘buzzword’ discussed at thought leadership seminars to be the next big revolution that has redefined the manner in which businesses function. In fact, IDC predicts that Big Data and business analytics market will grow to $203 billion by 2020 from $130.1 billion in 2016.

So as big data grows, what are the biggest trends that will define the industry? Over the years, IrisLogic has worked with a great many clients to deliver Big Data & Analytics solutions and here are some patterns that have emerged:

1) Insight Driven Business Decisions: Employing, understanding, and managing big data analytics has now become universal and something most successful companies rely on heavily.  But companies are now moving beyond just data management to drawing relevant insights from mountain piles of data. The focus is now on mining data effectively to uncover trends, patterns, and predictions to facilitate evolved customer experience, give businesses a competitive edge, benchmark competition, deploy advanced security (which typically requires fast storage and high storage capacity), and enable operational efficiency.

2) Collaborative business insights: Analytics will not remain the prerogative of a select few departments like Marketing & Sales but are permeating across the entire organisation. Companies are integrating insights from across departments to understand the dynamics of business operations and reveal ways and means to increase efficiency, growth and productivity while eliminating redundancy.

3) IoT and Machine Learning: Millions of internet connected devices from wearables to machines generate gold mines of data on a daily basis. Big data analytics allows for better feedback and proactive actionable insights that encourage companies to deploy machine learning techniques and IoT to smoothen day to day operations.

4) Cloud Analytics: The cloud becomes more and more ubiquitous as companies continue to opt for cloud-based solutions. However, International Institute for Analytics (IIA) predicts that companies will increasingly embrace the power of cloud analytics, where data analytics, data models, and processing software rest in the cloud. This will especially be true for small business entrepreneurs.

5) Database Security: Business will continue to search for and invest in the most secure solution that averts the risk of data breach and losses. Database security has been a prime business concern over the last few years as our dependence on technology continues to increase. With the growing adoption of big data analytics, big data needs to be secured and cyber security will garner more attention and will be a major area of investment. According to the ‘Cybersecurity Market Report’ by Cybersecurity Ventures, cyber security spending will top $1 trillion from 2017 to 2021.

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Big data has become ubiquitous as companies large and small talk about the benefits and challenges of all the data they collect and process. Today’s businesses have more data than ever before – with almost 90% of the world’s data having been collected since 2014.

But while many businesses are beginning to rely on big data to help make business decisions, how many of them are truly able to derive actionable insights from the heaps of available data and use it to maximise profitability?

As per recent research, it seems 90% of the organisations are not even analysing their data using conventional database systems leaving a lot of hidden value unexplored!

Data is everywhere, but being able to use it effectively is what makes a business stand apart from the rest. If you are wondering how to make the most of all the data your company generates, then read on. We are here to provide a simple structure that will help you make data your competitive advantage.

Step 1: What’s the Goal?

The first and most crucial step of the process is to identify the company’s goals for using the data: what measures impact your company the most? Which derivative of the data can help you understand your business operations? What do you want from the data; finding new prospects, reducing expenses or improving customer satisfaction?

Knowing what you want to measure, and what insights you want to glean from that measurement can help you reverse-engineer the process and make the data work for you. 

Step 2: Get it together

Data often lies in disparate systems. One system collects marketing data, another HR, another gathers customer service details, and a completely different system collects logistics data.

The challenge with siloed information is that it makes it nearly impossible to get the pieces of the puzzle to fit together – to get a broader view of the picture and then dive into granular details. Your business in such a case can identify shortcomings and gaps in the individual processes but is unable reveal the inefficiencies or opportunities that impact the business as a whole.

Step 2 hence is to get your data in one place and invest your efforts to design a data collection strategy. Make sure your data is normalized and consolidated into a single source of truth. Ideally, use the same analytics provider or storage system for all your valuable company and customer data. Most importantly, keep your data in-house!

Step 3: Keep Your Data Clean

The authenticity of your data is its true value. Hence, it’s essential to maintain clean data, frequently verify data validity, and check data collection accuracy. It’s a team sport! Ensure your colleagues are empowered and encouraged to keep the data they manage clean at all times.

It’s crucial to have confidence that the data is true and accurate. Each team contributing or overseeing the collection of data should maintain it.

Step 4: Generate insights and communicate

Data can tell companies a lot, but drawing intelligent insights depends upon the seeker. You should bring in the judgement of experience to bear on data and gain an understanding of the trends, patterns and unravel the ‘whys’ hidden in the data.

Analyses should include inputs from people who understand the company’s objectives, its goals, its customers, etc. In other words, you need to balance science and art when it comes to data mining – you need to add intuition gained through experience to numbers.

Step 5: Use the right tools

Big data is all about using the right tools to read into the data and gain from it. Over the past decade, there has been an influx of business intelligence and data analytics tools which have made big data accessible to everybody. These tools help companies, big or small, get insights faster and communicate better with the data.

There you go- use the insights generated, set an action place and stick to it till you reach your business goals. You are now a data-driven organisation, all set to make the cut.

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Big Data and how IrisLogic solves to implement a combinations of technology and Tools to solve business problems in five key areas:

• Data Extraction : Flume, Sqoop
• Data Processing : Hadoop, MapR, Greenplum, Vertica, Lucene, Solr
• Data Storage : HDFS, NoSQL (NeoJS, MongoDB, Oracle)
• Analytics : Mahout, NLTK, OpenNLP, UIMA
• Visualization : Intellicus, Tableau, MicroStrategy, PowerBI

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Data and analytics is the future of the business world. Gone are the days of unreliable gut feelings and error-prone decision making. Here are the days of reliable metrics and data-driven decision making. This is happening, everywhere, all the time, and across all industries. Companies with data and intelligence are dominating companies without data.

Thanks to competitive dynamics and capitalism, every company in the world will inevitably be forced to turn into a data-driven organization over the next few years or else they risk simply being pushed out of the market by competitors. We would broadly call this pivot towards data-driven decision making as the emergence of the Intelligence Economy since it’s, quite literally, just companies using intelligence to improve economic outcomes.

Data science platforms that provide the analytics and tools to empower data-driven decision making at the enterprise level are the heart of this burgeoning Intelligence Economy.

As data-driven decision making becomes ubiquitous across the enterprise over the next several years, data science platforms will become equally ubiquitous. Because data is no good if we can’t analyze it. You need to be able to transform clunky, large CSV and Excel files into actionable insights in order to glean value from the data, and truly benefit from the Intelligence Economy (or, again, you risk being run out of business by someone who does).

These days data analytics platforms provides an end-to-end platform which does just that — gives enterprises the analytics and tools necessary to turn raw data into insights. More important is that Data Scientist are in demand and not available readily. Thus low-code or no-code platforms wherein folks with business knowledge can actually drive data to analytics are going to play crucial part.

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Big data is a collection of large datasets that cannot be processed using traditional computing techniques. Testing of these datasets involves various tools, techniques, and frameworks to process. Big data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, variety, and velocity.

At IrisLogic we are experts in Big data testing using inhouse developed automation frameworks. At IrisLogic, we have the frameworks to make these processes and automations simple and efficient and as data engineering and data analytics advances to the next level. Our engineers have experience in automating QA for User Interface, back end data queries, and web services API. Our testing platform and framework reduces time to write scripts and test considerably, saving customers precious time and resources.

Architecture Testing is the important phase of Big data testing, as poorly designed systems may lead to unprecedented errors and degradation of performance. Performance testing for Big data includes verifying Data throughput, Data processing, and Sub-component performance. IrisLogic is a perfect partner to navigate you through this journey of BigData Application and Infrastructure Manual and Automated testing using state of the art framework, platforms, and expertise. We are well versed in all areas of Hadoop, MapReduce, Spark, YARN, Cloudera, Kafka, Hive, HBase, Tez, Pig, Zookeeper, Sqoop, Flume, Oozie and much more.

IrisLogic has been providing Big Data and Testing services to some of leading financial, manufacturing, and IT companies. Contact us today to find out more and how we can partner to help you navigate through this journey.

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The adequate use of big data has the potential to transform businesses, delivering a new wave of productivity growth and consumer comfort. Adopting big data will incline a key basis of competition for present organizations, and will create new competitors who are adept to appeal employees that have the analytical skills for a big data world.

Leaders of organizations commit to perceive the potential opportunity as well as the strategic peril that big data produce and should assess and then block any chasm between their current IT skill and their data strategy and what is crucial to seize big data actions suitable to their enterprise.

They will need to be creative and proactive in determining which pools of data they can combine to create value and how to gain access to those pools, as well as addressing security and privacy issues. On the topic of privacy and security, part of the task could include helping consumers to understand what benefits the use of big data offers, along with the risks. In parallel, organizations obliged to recruit and absorb deep analytical talent and retrain their analyst and management ranks to grow into more data savvy, chartering a culture that values and rewards the adoption of big data in decision making.

Decision makers need to recognize the potential of exploit big data to recognize the next wave of expansion in their organizations. They must commit to provide the institutional framework to allow companies to easily create value out of data while protecting the privacy of citizens and providing data security.

Leaders also have a significant role to play in helping to mitigate the shortage of talent through education and putting in place technology enablers including infrastructure such as:

• communication networks
• accelerating research in selected areas including advanced analytics
• creating an intellectual property framework that encourages innovation

Creative solutions to align catalyst may also be crucial, including, for instance, requirements to share certain data to advocate the community welfare.

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