Home » Peter DeCaprio: 10 Things You Don’t Want to Hear About the Top Choices to Make in Latest Big Data Technologies

Peter DeCaprio: 10 Things You Don’t Want to Hear About the Top Choices to Make in Latest Big Data Technologies

Peter DeCaprio

Big data technologies are becoming more and more widely used in the industry, while not all companies intend to use big data technology, but many of them have started to take an interest. This is because the idea behind big data technologies is pretty simple – it’s about making use of large quantities of information that other systems fail to make use of or can’t handle at all says Peter DeCaprio.

10 Things You Don’t Want to Hear About the Top Choices to Make in Latest Big Data Technologies

1)  Hadoop is not a silver bullet. Hadoop, being an open-source framework for the storage and processing of large data sets while utilizing computer clusters, has become extremely popular lately because of its ability to run various programs on different machines at once. Yet this doesn’t mean that it’s capable of solving all your needs – you’ll have to consider other options too or possibly design something yourself, especially if you are looking for some more specific features which aren’t readily available in Hadoop itself.

2)   MapReduce isn’t the only way to distribute work across machines. MapReduce is the name of one of the core features in Hadoop – it’s a programming model that allows data processing to be split across multiple machines. It works fine for most people, but you may want to think twice before using it as your only option if you are planning on doing complex calculations or require real-time performance – not all problems can be solved easily with this approach alone.

3) It’s not always easy to make sense of huge amounts of data. While big data technology companies often boast about how great their products are at handling enormous quantities of information, remember that it doesn’t mean that they will give you any insights into what they’re saying – it takes time and effort on part of analysts to make sense of the data, only then will it be useful for decision making.

4)  Big Data might not always be necessary if you can process your information faster. If you are looking at the architecture of some sort of data warehouse or BI solution that claims to support big data technology, think about whether or not you actually need any – yes, there is a possibility that they are overestimating their abilities but more often than not big data technologies have become so popular because many companies require them in order to perform complex computations and/or generate insights from large quantities of data.

5)  Not all NoSQL databases are alike. The key advantage offered by so-called NoSQL databases is that they are capable of storing and processing information that is too big or complex for relational databases, but not all of them are alike – don’t think that you can use any NoSQL database because it’s pretty much like any other says Peter DeCaprio.

6)  Big data technology companies might be great at marketing, but that doesn’t mean they’re always good. Big data technology companies often boast about the abilities of their solutions and tout themselves as the best solution possible but this doesn’t necessarily mean that they are – do some research prior to making a decision where to invest your money and/or time.

7)  You might need more than just big data infrastructure in order to make sense of information. While this list does offer insights into what’s behind many different types of big data technologies. It doesn’t mean that you need all of them to make sense of your information. it might be enough if you utilize a simple web server and some scripting language. such as Python in order to write custom scripts explains Peter DeCaprio.

8)  Big Data is expensive. While we’ve already mentioned that big data technology companies boast about their products’ cost efficiency. You will probably end up spending a fair amount on licensing and support after making your initial investment (and the costs might even go higher once your volume increases). This means that if you don’t require complex computations or don’t have too much data. Simple infrastructure will likely suffice for a much cheaper price.

9)  You might not need NoSQL databases after all. Despite their obvious advantages, NoSQL databases aren’t perfect. They might be good for simple reads and quick look-ups but most of them. Don’t scale or perform well if you need to make precise queries. If you require more complex operations, a relational database will likely serve your needs better.

10)  Big data technology may not turn out as a silver bullet for all companies. It’s clear that big data is supposed to solve many problems. That organizations have been facing in the past but just because it works for one company. Doesn’t mean that it will work for another – accept this as a reality. Before deciding which tools to use in order to solve particular problems.

Conclusion:

Peter DeCaprio says it’s important to realize that big data technology doesn’t always live up to the promises made by large companies. Who are trying to sell their solutions by boasting about how great they are. Do some research on your own and don’t be fooled into thinking that you need something complicated. When simple infrastructure will suffice in many cases. 

Also, keep in mind that while NoSQL databases might be good at storing and processing information. Most of them do not scale well or perform complex queries. If you’re looking for more than just storage space and/or quick lookups. It might be better to use relational database management systems instead.