Fixing Data Analytics Challenges Through Ksolves Apache Spark Development Services

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5 MIN READ

October 18, 2021

Fixing Data Analytics Challenges Through Ksolves Apache Spark

Are you looking to integrate the forecast of your data into business plans? Well, that has been the case with organizations from all around the world regardless of their scale and size. However, every possibility comes with its own set of challenges, and Data Analytics is not unknown to the concept! Despite the huge possibilities in Data Analytics and Data Mining, there are numerous challenges that come along. 

Ksolves is not here to list down challenges but to provide a prominent solution for the same, as well. We are a software solution and product development company with an ample of experience in Apache Spark Development Services. How will our Apache Spark services help you sort out the challenges, is the major question. We will come to that, but first it’s important to take a peak on those Data Analytics challenges organizations face. So, let’s have a look at some of them! 

Data Analytics Challenges To Look Out

When you enter the realm of Big Data, you will encounter certain demons or as the world calls it, challenges! These demons will not only haunt you but also create a worrisome atmosphere for your organization. Some of these Big Data Analytics challenges include: 

Volume Of The Data

Challenge: Since IoT has entered the fray, the sources of incoming data saw a massive push. This particular fact has multiplied the amount of data even for small organizations without proper Data Warehouse facilities. Now, managing the huge amount of data is in itself a challenging task. Well, if you are just thinking about storing a huge amount of data, here is another issue involved! Managing big data will impact the performance of your hardwares and softwares, as well! The data will keep on growing with each passing second, leading to slow dashboards, reports, and models. 

Solution: Apache Spark is well-known around the world to handle large scale data with ease. The unified libraries will help you store the data for a long time and process it as per your requirement. Therefore, high volume of data is not an issue anymore! 

Missing Data Strategy

Challenge: When you have a lot on the table to manage, a proper strategy becomes crucial at every stage. Same is the case with Big Data Analytics, as well. The second monumental challenge organizations face while managing their data is the lack of proper strategy. When you have something that’s piling up every second, you need to work fast and efficiently at the same time. In fact, missing a single shred of data can cause you a valuable marketing strategy for your company. 

Solution: Ksolves India Limited provides Apache Spark Consulting Services to sort out best possible ways for your data integration. We have worked with companies of all domains that help us to understand the requirements of every company and lay out a perfect plan. Well, that’s one of our major gains from the last 10 years! 

Multiple Source For Data

Challenge: Earlier, we discussed IoT and the varied sources of extracting data! Starting from the internet to your day-to-day activities recorded through mobile phones, smartwatches, and other devices you are surrounded by, data is everywhere. However, reaching the conclusion considering the variety of data you have is another challenge mounting upon your organization. How will you select the shred of information to go forward with? What will you do when you have 2 of your data sources indicating poles apart information?   

Solution: What you need is a centralized data library that offers a framework to assess your data from all sources and conclude an effective solution. Wondering what we are talking about? Obviously, we are talking about Apache Spark as a service! Apache Spark will offer a platform enabled with both batch processing and stream processing abilities. So, you can simply choose the best way to access data from multiple sources. 

Visual Representation

Challenge: It’s often phrased that the best form of data is the visual representation that includes graphs and charts. Who are we to deny? However, converting the raw data into graphs and charts is a tough task when handled manually. Especially, when you have data incoming from all sources at each second. Just imagine a scenario where you need to analyze a forecast of your data and the latest report is not included in the analytics! Wouldn’t that be a major issue? Obviously, it will be! 

Solution: Since the arrival of real-time data streaming, organizations are jumping to get their hands on the technology. Indeed, you are looking at the best possible solution for your visual representation issue. Ksolves’ Apache Spark Development Services will help you design a platform to analyze your data visually with real-time updates. Apart from that, Ksolves will also make sure that your Apache Spark framework is easy-to-use and fulfills all of your requirements. 

An Ultimate Solution! 

Ksolves, being the best Apache Spark Development Company in India and the USA, is absolutely aware of all the challenges coming through and a prominent solution to deal with the same! Our 350+ developers and architects will combine their experience to bring out an over-the-top solution for your organization. These challenges might sound tricky and exhaustive, but with Ksolves holding your back, you’ve got nothing to worry about! Make a quick and share your requirements with us. We are waiting for you! 

Contact Us for any Query

Email : sales@ksolves.com

Call : +91 8130704295

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Feeding Data To Apache Spark Streaming

Is Apache Spark enough to help you make great decisions?

AUTHOR

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Anil Kushwaha

Spark

Anil Kushwaha, Technology Head at Ksolves, is an expert in Big Data and AI/ML. With over 11 years at Ksolves, he has been pivotal in driving innovative, high-volume data solutions with technologies like Nifi, Cassandra, Spark, Hadoop, etc. Passionate about advancing tech, he ensures smooth data warehousing for client success through tailored, cutting-edge strategies.

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