Cloudflare, from the company of the same name in San Francisco, provides DDoS and bot mitigation security for business domains, as well as a content delivery network (CDN) and web application firewall (WAF).
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
We've had cases where our company was under cyber attack and received phising emails which contained links to malicious and credential stealing websites, impersonating trusted pages, or hosting malicious files to be downloaded. These malicious links were all blocked 95% of the time due to various methods used by CF to detect malicious pages and/or content. We have had a few minor cases where legit pages were blocked, but this was mainly due to domain age, and pages/domains created specifically for joint ventures or projects.
DNS is quick to set up and seems to propagate quicker than other DNS name servers. Page rules are very convenient for quickly setting up redirects with wildcards.
Setting up a proxy with Cloudflare Workers for my API, which runs on Google Cloud Functions was much simpler than any solution I came across using Google Cloud Platform itself. It also doesn't cost us a lot of money for this use case.
Web Analytics are privacy-focused, so it is nice to get insights into our application without worrying about the data being sold. The interface is very clean. I like being able to query page views by bot score, to see how many bots are viewing pages compared to actual users. Querying this data is also very quick and simple when compared to Google Analytics API.
Cloudflare features are an integral part of my website, as of now I can’t think about doing without it. It would require an unimaginable time and effort to find and implement alternatives for every feature, considered how large and diverse Cloudflare feature set is
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
Everything is extremely concise and all settings apply immediately and take effect globally. There is no reason to explicitly plan/think in terms of individual regions as one would have to traditional cloud offerings (AWS, OCI, Azure). All Cloudflare products integrate seamless as part of a single pipeline that executes from request to response.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Because of Cloudflare Zero trust network security services, Cloudflare Zero Trust Network Access (ZTNA) is the technology that makes it possible to implement a Zero Trust security model. "Zero Trust" is an IT security model that assumes threats are present both inside and outside a network. Consequently, Zero Trust requires strict verification for every user and every device before authorizing them to access internal resources.
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
All except the greatest DDoS attempts are stopped by Cloudflare's security. Cloudflare is a worldwide cloud network that can safeguard and speed up any website. Cloudflare is ahead of the competition because of its global reach and cutting-edge tools for speeding up and optimizing both static and dynamic web traffic.
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
It has allow us to use the free plan in several small sites
For medium/big sizes, the plans they offer adjust properly to the needs we require; saving money and making the most of the spent one
Considering we manage several clients; we were able to centralize all of our client's accounts in our own; allowing us to save time in their management and giving them the security that we will only access the parts they require us to access
