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The SQL and NOSQL databases are used by facebook
1.Whether it uses GFS or HDFS or BOTH or some other?
2.What are the different Abstraction application layer available to work on HDFS AND GFS ??
3.I heard about HADOOP , How Map Reduce works ?
kindly narrate the advantages.
And also narrate about the second question deeply.
This is not a real question.
There are really thousands of articles out there.
To guide you into the right direction:
Facebook uses a lot of different technologies. SQL and NoSql. It is known that they use a lot Cassandra. Have a look at this video from the director of engineering from Facebook.
Here are also some more links of the Facebook architecture:
Facebook Architecture
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I'm just starting to get into web development, and I am planning a website.
This website will have users that can edit data. Think of it like a tree:
Theres the organisation (company), then under the organisation there are users. Each user can have multiple "clients", and the user can edit data about the "client" and share that data. The type of data are numbers and text mostly, and possibly some images.
What database paradigm would be best suited to this? I was thinking documents or relational. I want low-cost, but also lots of room for horizontal (and possible vertical) scaling.
Thanks :)
Considering your requirement, Google Cloud SQL will be the best option for you. It provides data manipulation option and horizontal scaling.
Google Cloud SQL is a fully-managed database service that offers high performance, scalability, and convenience. Hosted on Google Cloud Platform, Cloud SQL provides a database infrastructure for applications running anywhere.
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I've already read a lot about it, but I'm particularly interested for Laravel and its price difference.
We run a web application based on Laravel and this already with the Google Cloud App Engine. For our web application we need a solid database. Here the question arose, which one?
What are the main differences between Firebase and GCP SQL? How is this reflected in the price?
I'd recommend heading into this GCP Databases page which has a cool matrix on what are the differences between solutions on GCP. It really depends on what kind of data you have and how scalable the solution needs to be.
There is also a nice decision diagram on this blog post which can be seen below. It feels like instead of using Firebase Realtime DB or Cloud Datastore, one should now be using the Firestore which is also integrated into the GCP UI experience.
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They both seems to store files in a decentralized network. So what's the difference between them in terms of data structure and algorithms they use? And also what's the difference between them in terms of use cases?
If I store a file in IPFS or Filecoin, will it remain there forever or it may get lost?
IPFS does storage and content-based addressing. Filecoin is just a way to incentivize people to store IPFS content.
So it's not really easy to tell the difference because they are not comparable. Filecoin just works on top of IPFS.
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In ZMQ messaging library there is large number of patterns derived from a base "Pirate" pattern. To quote the documentation:
I like to call the Pirate patterns (you'll eventually get the joke, I
hope).
I have a pretty through understanding of the ZMQ architecture, having worked with it over a half dozen projects and couple of years. Despite this, and reading basically the entire guide, I don't get the joke.
Perhaps there isn't one, but I can't help the itch that I am missing something fairly obvious. Thanks.
From the docs:
reliable request-reply (RRR) patterns that I like to call the Pirate patterns
Pirates say 'arrrr!' like RRR, hence pirate patterns.
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We have a database of about 1000 GB (thousand Gigabytes) and we are considering Hadoop to carry out time series analyses. Problem is that Hadoop takes some time to get into and for the size of our database Hadoop is actually even an oversized solution. My question is whether anyone knows a solution similar for Hadoop at a smaller scale. It also needs to have some SQL-like query language. Only thing that comes to my mind is JBoss Infinispan. But I wanted to check out whether there are other known solutions.
check out greenplum
We have 2 types of big data solutions:
1 is based on hadoop which supports data storage at PB levels
2 is based on greenplum for real time data analysis on smaller data scale.