Likelihood to Recommend Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
Read full review Azure Data Lake is an absolutely essential piece of a modern data and analytics platform. Over the past 2 years, our usage of Azure Data Lake as a reporting source has continued to grow and far exceeds more traditional sources like MS SQL, Oracle, etc.
Read full review Pros Its performance, ease of use, and simplicity in learning and deployment. Using this tool, we can quickly analyze large amounts of data. It's adequate for map-reducing large datasets and fully abstracted MapReduce. Read full review Setting up Azure Data Lake Storage account, container is quite easy Access from anywhere and easy maintenance Integration with Azure Data Factory service for end to end pipeline is pretty easy Can store Any form of data (Structured, Unstructured, Semi) in faster manner Read full review Cons UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors. Being in early stage, it still has a small community for help in related matters. It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement. Read full review study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment. The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology Read full review Usability It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Read full review Support Rating The documentation is adequate. I'm not sure how large of an external community there is for support.
Read full review Alternatives Considered Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Read full review Azure Data Lake Storage from a functionality perspective is a much easier solution to work with. It's implementation from
Amazon EMR went smooth, and continued usage is definitely better. However,
Amazon EMR was significantly cheaper overall between the high transaction fees and cost of storage due to growth. The two both have their advantages and disadvantages, but the functionality of Azure Data Lake Storage outweighed it's cost
Read full review Return on Investment Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with. Read full review Instead of having separate pools of storage for data we are now operating on a single layer platform which has cut down on time spent on maintaining those separate pools. We have had more of an ROI with the scalability as we are able to control costs of storage when need be. We are able to operate in a more streamlined approach as we are able to stay within the Azure suite of products and integrate seamlessly with the rest of the applications in our cloud-based infrastructure Read full review ScreenShots