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Apache Spark Primary Use Case

SA
Technical Consultant at a tech services company with 1-10 employees

We are working with a client that has a wide variety of data residing in other structured databases, as well. The idea is to make a database in Hadoop first, which we are in the process of building right now. One place for all kinds of data. Then we are going to use Spark.

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Software Architect at Akbank

We just finished a central front project called MFY for our in-house fraud team. In this project, we are using Spark along with Cloudera. In front of Spark, we are using Couchbase. 

Spark is mainly used for aggregations and AI (for future usage). It gathers stuff from Couchbase and does the calculations. We are not actively using Spark AI libraries at this time, but we are going to use them.  

This project is for classifying the transactions and finding suspicious activities, especially those suspicious activities that come from internet channels such as internet banking and mobile banking. It tries to find out suspicious activities and executes rules that are being developed or written by our business team. An example of a rule is that if the transaction count or transaction amount is greater than 10 million Turkish Liras and the user device is new, then raise an exception. The system sends an SMS to the user, and the user can choose to continue or not continue with the transaction.

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RV
Director at Nihil Solutions

When we receive data from the messaging queue, we process everything using Apache Spark. Data Bricks does the processing and sends back everything the Apache file in the data lake. The machine learning program does some kind of analysis using the ML prediction algorithm.

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Learn what your peers think about Apache Spark. Get advice and tips from experienced pros sharing their opinions. Updated: December 2021.
554,676 professionals have used our research since 2012.
Chief Data-strategist and Director at theworkshop.es

You can do a lot of things in terms of the transformation of data. You can store and transform and stream data. It's very useful and has many use cases.

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Engineering Manager at Sigmoid

I use it mostly for ETL transformations and data processing. I have used Spark on-premises as well as on the cloud.

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SS
Co-Founder at a tech vendor with 11-50 employees

We have built a product called "NetBot." We take any form of data, large email data, image,  videos or transactional data and we transform unstructured textual data videos in their structured form into reading into transactional data and we create an enterprise-wide smart data grid. That smart data grid is being used by the downstream analytics tool. We also provide machine-building for people to get faster insight into their data. 

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Director of BigData Offer at IVIDATA

We primarily use the solution to integrate very large data sets from another environment, such as our SQL environment, and draw purposeful data before checking it. We also use the solution for streaming very very large servers. 

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Consultant at Exusia

Our use case for Apache Spark was a retail price prediction project. We were using retail pricing data to build predictive models. To start, the prices were analyzed and we created the dataset to be visualized using Tableau. We then used a visualization tool to create dashboards and graphical reports to showcase the predictive modeling data.

Apache Spark was used to host this entire project.

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GA
Senior Solutions Architect at a retailer with 10,001+ employees

We use Apache Spark to prepare data for transformation and encryption, depending on the columns. We use AES-256 encryption. We're building a proof of concept at the moment. We prepare patches on Spark for Kubernetes on-premise and Google Cloud Platform.

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Learn what your peers think about Apache Spark. Get advice and tips from experienced pros sharing their opinions. Updated: December 2021.
554,676 professionals have used our research since 2012.