Item: Apache Spark
Rating: 10
Author: Sunil Dhage

Use Cases and Deployment Scope

It's being replaced as the traditional ETL tool and we are using Apache Spark for data science solutions.

Pros and Cons

It makes the ETL process very simple when compared to SQL SERVER and MYSQL ETL tools.
It's very fast and has many machine learning algorithms which can be used for data science problems.
It is easily implemented on a cloud cluster.

The initialization and spark context procedures.
Running applications on a cluster is not well documented anywhere, some applications are hard to debug.
Debugging and Testing are sometimes time-consuming.

Return on Investment

Time saved in developing applications is less.
ROI on time, resources, money.
Can replace the traditional database systems.

Other Software Used

Microsoft SQL Server, Apache Pig, Cloudera Manager

Likelihood to Recommend

It's well suited for ETL, data Integration, and data science problems of large data sets. It's not at all suitable for small data sets which can be done on desktops and laptops using the Python tool.

Products Replaced

Yes - Microsoft Server

Key Differentiators

Price
Product Features
Product Usability
Product Reputation
Prior Experience with the Product
Vendor Reputation
Existing Relationship with the Vendor
Analyst Reports

It works on all kinds of data unlike SQL Server which needs structured data.

Evaluation Lessons Learned

I would think ofROI and resource allocation as the most important factors.

Sparkling Spark

Overall Satisfaction with Apache Spark