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How to Deal With the Most Common Challenges in Web Scraping

Introduction

In the world of business, big data is key to competitors, customer preferences, and market trends. Therefore, web scraping is getting more and more popular. By using web scraping solutions, businesses get competitive advantages in the market. The reasons are many, but the most obvious are customer behavior research, price and product optimization, lead generation, and competitor monitoring. For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.

Modifications and Changes in Website Structure

From time to time, some websites are subject to structural changes or modifications to provide a better user experience. This may be a real challenge for scrapers, who may have been initially set up for certain designs. Hence, some changes will not allow them to work properly. Even in the case of a minor change, web scrapers need to be set up along with the web page changes. Such issues are resolved by constant monitoring and timely adjustments and set-ups.

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How Big Data and Customer 360 Drive Your Businesses in 2021

"Big data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.” - Ginni Rometty, CEO, IBM

Have you ever imagined the power of data that can transform an entire system? Be it a small data analytics solution or the data needed to make a safe driving decision, big data plays a leading role in almost every industry it incorporates.

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If Testing Was a Race, Data Would Win Every Time

Okay, so that title doesn’t make complete sense. However, if you read to the end of this article, all will become clear. I’m first going to discuss some of the persistent barriers to in-sprint testing and development. I will then discuss a viable route to delivering rigorously tested systems in short sprints.

The two kingpins in this approach will be data and automation, working in tandem to convert insights about what needs testing into rigorous automated tests. But first, let’s consider why it remains so challenging to design, develop and test in-sprint.

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‘mapPartitions’ in Apache Spark: 5 Key Benefits

'mapPartitions' is the only narrow transformation, being provided by Apache Spark Framework, to achieve partition-wise processing, meaning, process data partitions as a whole.  All the other narrow transformations, such as map, flatmap, etc. process partitions record-wise. 'mapPartitions', if used judiciously, can speed up the performance and efficiency of the underlying Spark Job manifold. 

'mapPartitions' provides an iterator to the partition data to the computing function and expects an iterator to a new data collection as the return value from the computing function. Below is the 'mapPartitions' API applicable on a Dataset of type <T> expecting a functional interface of type 'MapPartitionsFunction' to process each data partition as a whole along with an Encoder of the type <U>, <U> being representing the returned data type in the returned Dataset.     

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5 Ways to Adapt Your Analytics Strategy to the New Normal

Covid 19 has upended all traditional business models and made years of carefully curated data and forecasting practically irrelevant. With the world on its head, consumers can’t be expected to behave the same way they did 9 months ago, and we’ve witnessed major shifts in how and where people and businesses are spending their money. This new normal— the “novel economy,” as many have dubbed it—requires business leaders to think on their feet and adjust course quickly while managing the economic impact of lockdowns, consumer fear, and continual uncertainty. The decisions they make today will affect their company’s trajectory for years to come, so it is more important than ever to be empowered to make informed business decisions.

In recent years, organizations across industries have started to implement advanced analytics programs at a record pace, drawn by the allure of increased efficiency and earnings. According to McKinsey, these technologies are expected to offer between $9.5 and $15.4 trillion in annual economic value when properly implemented. However, most organizations struggle to overcome cultural and organizational hurdles, such as adopting agile delivery methods or strong data practices. In other words, adopting advanced analytics programs is happening across the board, but successful implementation takes a long time.

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Identify and Resolve Stragglers in Your Spark Application

Stragglers are detrimental to the overall performance of Spark applications and lead to resource wastages on the underlying cluster. Therefore, it is important to identify potential stragglers in your Spark Job, identify the root cause behind them, and put required fixes or provide preventive measures. 

What Is a Straggler in a Spark Application?

 A straggler refers to a very very slow executing Task belonging to a particular stage of a Spark application (Every stage in Spark is composed of one or more Tasks, each one computing a single partition out of the total partitions designated for the stage). A straggler Task takes an exceptionally high time for completion as compared to the median or average time taken by other tasks belonging to the same stage. There could be multiple stragglers in a Spark Job being present either in the same stage or across multiple stages. 

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Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

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Deep Dive Into Join Execution in Apache Spark

Join operations are often used in a typical data analytics flow in order to correlate two data sets. Apache Spark, being a unified analytics engine, has also provided a solid foundation to execute a wide variety of Join scenarios.

At a very high level, Join operates on two input data sets and the operation works by matching each of the data records belonging to one of the input data sets with every other data record belonging to another input data set. On finding a match or a non-match (as per a given condition), the Join operation could either output an individual record, being matched, from either of the two data sets or a Joined record. The joined record basically represents the combination of individual records, being matched, from both the data sets.

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5 Steps for Implementing a Modern Data Architecture

Current market dynamics don’t allow for slowdowns. Digital disrupters have made use of innovations in AI, serverless data platforms, and seamless analytics that have completely upended traditional business models. The current market challenges presented by the Covid-19 pandemic have only exacerbated the need for fast, flexible service offerings. To remain competitive and relevant, businesses today have to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.

However, as businesses strive to implement the latest in data technology—from stream processing to analytics and data lakes—many find that their data architecture is becoming bogged down with large amounts of data that their legacy programs can’t efficiently govern or properly utilize.

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All You Need to Know About Smart Grid Big Data Analytics

Did you know the smart grid data analytics market is anticipated to grow by 25% from 2020 to 2024? This data analytics is now playing a more important role than anything else in the modern industrial system today. Indeed, it has unlocked novel groundbreaking opportunities for almost everyone. While among this all, smart grid data analytics is shaking up the entire industry with its utilities and technological innovations, making a significant impact on the lives of many people.

Please continue reading to know more about smart grid big data analytics, and how it is influencing the living of us all!

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Finding the Humanity of Big Data

It’s no secret that Big Data offerings have become one of the largest marketing bastions the world has ever seen.

In a fast-paced and ever-changing era, industries race against one another more than ever before to raise benchmarks, contexts, ROI, and ultimately profit margins in an interconnected world that never sleeps. Big data consulting services have been around for several years now, helping organizations reach their business goals by carefully absorbing and organizing trillions of bytes worth of data. As the process progresses and internet access continues to expand around the globe, the amount of data to process will only continue to swell.

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Data Democratization and How to Get Started?

Today data is an important factor for business success. In every business, it has been observed that data is playing a game-changing moment to improve business performance.
Data is important and necessary in this increasingly competitive world. It is essential for companies to help maintain a competitive edge so that they can help reduce costs and grow profitable sectors that they have disappeared from.
Data comes in large volumes everywhere and in complex structures. It became complicated to understand. Being able to understand data is the preserve of longtime, highly paid data scientists and analysts. The idea of helping everyone to access and understand data is known as data democratization.

In this blog, we have introduced the features of data democratization that a business can adopt to overcome these challenges and establish an enterprise-wide data democracy.

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6 Ways Big Data Analytics Change the Insurance Industry

Technology has a big impact on the way the insurance sector does business. Although big data analytics as a service is still fairly new, insurers rely on it heavily. As companies increase the number of policyholders in their databases, the need for meaningful analysis becomes more crucial. Big data analytics applications make this task feasible.

Big data services help resolve data issues that insurers face on a daily basis. A big data analytics platform can be challenging for those still getting used to the technology. However, there are many advantages to coming to terms with what big data offerings can do for your business. Learn more about how the insurance industry benefits from data analytics from the time a customer signs an application through their first claim filing.

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Growing Importance of Analytics As A Service

As the world’s datasphere grows in stature and size, big data, artificial intelligence, and cloud computing are combining to provide enterprises the much-needed respite in the form of Analytics-as-a-service 

Technology has become imperative — or has it?

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6 Top Big Data and Data Science Courses to Start Learning Right Now

Among the most anticipated technology trends for the future, Big Data finds its place and offers an excellent opportunity to shape your career.

Big Data analytics has secured its place in the top technology trends for the year 2020. The increasing demand for AI and machine learning-enabled solutions drives the requirement for data scientists, and big data helps you pave your way to enjoying a successful career in the same.

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Making Data and Analytics Sexy

Data_AnalyticsIt’s now safe to say that data is the lifeblood of how organizations operate today. Developers are at the heart of all of that. The analytical tools and dashboards developers build are the primary drivers of data-driven decision-making and they are critical tools for the survival of 21st century businesses. If you are a developer or data analyst/scientist involved in building analytics dashboards at your organization, we want to hear from you! Please take this 3 minute survey and tell us about your experiences building analytics dashboards.

Over the next two weeks, we plan to survey hundreds of software developers. The key findings from the survey will be found in our upcoming Trend Report to be released March 25. It is our hope that we can identify some of the key trends happening in this space to help our community stay ahead of the curve. We also want to identify what challenges DZone developers are facing so that we can create content to help you and your colleagues overcome them. We always appreciate your help and contributions to this (in our opinion) awesome community.