Apache Spark is an open-source large-scale data processing engine that is built to create and work with machine learning algorithms, SQL, and process visual content. Apache Spark has various modules that make it easier to run the data analysis with very less memory consumption.
Aegis Softwares is an official partner of Apache Spark capable of leveraging the data science techniques in combination with the Apache Spark and its integrations to develop a platform it is efficient enough to stream the load of data over a few seconds. The main goal of our Apache Spark analytics solutions is to create a customized platform with fast data computing capabilities of Apache Spark. We also ensure the ease of the use of the system with quick access to the required data and its respective tools.
We are not just another Apache Spark development company; we are pioneers in big data with specialization in Hadoop technologies like HDFS, Map Reduce, Hive, and several other applications for easy significant data adoption. It equips us to create a holistic, big data analytics system for your company with the inclusion of the other tools along with Apache Spark.
Aegis can help you to implement Apache Spark analytics solutions for different applications and business requirements. As an established Apache Spark development company in India, we have worked with organizations and startups from different domains by supporting their big data goals with Apache Spark. We have worked with businesses from:
Apart from these sectors, we have also worked with companies that provide B2B services.
As a part of our Apache Spark development, we provide a wide range of services for big data analytics, which includes:
With our years of experience with Apache Spark and big data analytics in general, we know a thing or two better about leveraging our technical expertise to combat real-life data challenges. We have a pool of expert data analysts, data scientists, and data engineers who have worked with hundreds of other businesses to build and incorporate Apache Spark platform for their data needs.
Our wealth of information comes in handy at the times when you require fast data analytics solutions or when you have a hard puzzle that can only solve with data analysis. Furthermore, we have all the necessary tools and the latest software required to develop the Spark platform according to your business.
Therefore, our team will first listen to your requirements for Apache Spark solutions, analyze the current state of data utilization in the industry, create a better platform that beats your competitors and provide suggestions about putting data to use in innovative ways that have not yet been explored by the others in your industry.
If you want an expert data team on your side who has tackled the major data challenges since the past eight years, then Aegis it is! Just send out your requirements for Apache Spark in your organization to email@example.com. Get the worthy partner for your Apache Spark Development and big data analytics now and forget all about your data woes!
Spark is an in-memory data processing framework which supports batch processing, stream processing, and interactive analysis. Data scientists often use it for data analysis. One can use Scala, R, Python, and Java on Spark. Spark is actively used for processing data collected through sensors (and other IoT objects) and solves a major part of the existing big data problems. It can also handle iterative machine learning very well.
When faced with this question, you need to talk about 5 basic points: Speed, Processing, Difficulty, Recovery, and Interactivity.
Spark is 100 times faster than Hadoop is. Apache provides, for real-time and batches processing whereas Hadoop supports Batch processing only. While Apache Spark is easy to work with (thanks to high-level APIs), Hadoop is very tough to learn. Apache spark allows recovery of partitions, while Hadoop is fault-tolerant. Hadoop has only Pig and Hive that are interactive, while Apache Spark has full-blown interactive modes.
Spark outshines Hadoop when dealing with real-time querying of data. Coming to Stream Processing, detecting frauds during live streams is possible using Spark. Sensor Data Processing is relatively much faster here. ‘In-memory computing’ is a huge benefit of Spark, reducing reading and writing time from the disk.
Spark supports Scala, Java, Python and R. Since Spark is written on Scala, Scala is the most popular amongst them to be implemented on Spark. Scala and Python both have interactive shells which can be accessed through these simple commands: ./bin/spark-shell or ./bin/pyspark.
Uber, Pinterest, and Netflix is their latest addition.
These were a few questions you’re bound to ask in a Spark interview. However, this list is not far from being exhaustive, as there are multiple concepts, from polyglots to BlinkDB that dived. However, if you have worked with Spark enough to build independent projects on it, you will be able to face the interview reasonably well.