There is a lot of discussion surrounding Snowflake and Databricks in determining which modern cloud solution is better for analytics. However, both solutions were purpose-built to handle different tasks, so neither should be compared from an “apples to apples” perspective.
With that in mind, I’ll do my best to break down some of the core differences between the two and share the pros/cons of each as unbiasedly as possible. Before diving into the weeds of Snowflake and Databricks though, it is important to understand the overall ecosystem.
With the world on pace to reach 175 Zettabytes of data by 2025, it’s no wonder why organizations are placing such a high emphasis on building out their technology stacks. Now more than ever, companies need a way to collect and consolidate data into a single platform to derive insights quickly.
This is one of the core reasons that Snowflake and Azure Synapse Analytics have risen to such popularity. However, Synapse and Snowflake are different solutions and both should be analyzed from an unbiased lens. With that in mind, here are some of the core differences and pros/cons to Snowflake and Synapse.
As a Software Engineer, the mind is trained to seek optimizations in every aspect of development and ooze out every bit of available CPU Resource to deliver a performing application. This begins not only in designing the algorithm or coming out with efficient and robust architecture but right onto the choice of programming language. Most of us, as we spend years in our jobs — tend to be proficient in at least one of these.
Recently, I spent some time checking on the Performance (not a very detailed study) of the various programming languages. One, by researching on the Internet; Two, by developing small programs and benchmarking. The legacy languages — be it ASM or C still rule in terms of performance. But these are definitely ruled out for enterprise applications due to the complexity in development, maintainability, need for object orientation, and interoperability. They still will win for mission-critical or real-time systems, which need performance over these parameters. There were languages I briefly read about, including other performance comparisons on the internet. These include Python, PHP, Perl, and Ruby. Considering all aspects and needs of current enterprise development, it is C++ and Java which outscore the other in terms of speed. According to other comparisons [Google for 'Performance of Programming Languages'] spread over the net, they clearly outshine others in all speed benchmarks. So much for my blog title :-) So when these titans are pit against each other in real-time, considering all aspects of memory and execution time — Java is floored. Though I have spent the last ~17 years (In 2021) of my life coding and perfecting my Java and J2EE skill — I suddenly feel... Ahem, Slow! One of the problem statements to verify this is given below (along with the associated code) and the associated execution parameters.
[Disclaimer: Problem Statement given below is the property of www.codechef.com]
In Byteland they have a very strange monetary system. Each Bytelandian gold coin has an integer number written on it. A coin n can be exchanged in a bank into three coins: n/2, n/3, and n/4. But these numbers are all rounded down (the banks have to make a profit). You can also sell Bytelandian coins for American dollars. The exchange rate is 1:1. But you can not buy Bytelandian coins. You have one gold coin. What is the maximum amount of American dollars you can get for it? Input The input will contain several test cases (not more than 10). Each test case is a single line with a number n, 0 <= n <= 1 000 000 000. It is the number written on your coin.
The term fog computing (or fogging) was coined by Cisco in 2014, so it is new for the general public. Fog and cloud computing are interconnected. In nature, fog is closer to the earth than clouds; in the technological world, it is just the same, fog is closer to end-users, bringing cloud capabilities down to the ground.
The main difference between fog computing and cloud computing is that the cloud is a centralized system, while the fog is a distributed decentralized infrastructure.
Serverless computing, which is commonly referred to as just Serverless, is a promising cloud-based technology model that has emerged on the app development and software architecture horizon in recent years. Trying to avail themselves of the huge serverless framework potential, many big-time market players have been quick to jump on the cloud services bandwagon. Such software giants like Google, Microsoft, IBM, and Amazon already offer the customers to migrate all the local business operational efficiencies to be hosted on their flagship serverless platforms like AWS Lambda and Azure Functions.
Simply put, serverless architecture is an event- and request-driven tech solution allowing application developers to create actionable working environments in the cloud that have all the necessary computational resources needed for a smooth coding flow. This framework comes in handy especially when time is an issue and the tasks assigned are quite resource-intensive.
In 1986’s Star Trek IV: The Voyage Home, Scotty blithely picks up a mouse from a 1980s Macintosh and attempts to speak instructions into it, expecting the computer to follow them implicitly.
Good for a laugh back in the day to be sure, but today, we’re surprisingly close to realizing this vision for computing.
The advent of artificial intelligence has resulted in numerous technological developments in recent years. One of the AI-related fields that are becoming popular and promises to take the current digitized economy to the next level is cognitive computing. It makes use of machine learning and reasoning, natural language processing, speech recognition, and data mining processes such as AI; however, it takes things to the next level. Cognitive computing technologies can grasp and manage vast amounts of data, apply reason, gain insights, and constantly learn when interacting with individuals and machines. They provide us with a great opportunity to make smarter and more informed decisions.
The market for cognitive computing is growing rapidly. Driving it are factors such as the increasing volume of large complex data and the growing trend of increasing cloud-based services, big data analytics, and harnessing the internet speed. As per Allied Market Research, the market generated $13.8 billion by 2020, thereby growing at a CAGR of 33.1 percent during the forecast period, 2015 - 2020.
5G is a disruptive technology mandatorily needed to meet the capacity and performance requirements of future networks. Massive bandwidth needs and extremely low latency requirements, needed by burgeoning applications (like AI, IoT, AR/VR), require 5G to be facilitated by other emerging technologies like SDN/NFV and multi-access edge computing (MEC). By bringing the computing closer to the user, MEC promises to meet the desired latency and bandwidth constraints. Standardization bodies, like 3GPP (for 5G) and ETSI (for MEC), have been working towards streamlining the procedures for interworking of 5G core and MEC systems. The 5G and MEC specifications give an insight into the future integration strategy expected – making MEC work as a 5G application function to interact with the 3GPP 5G system for traffic steering and reception of mobility events. But a complete flow of information between MEC function entities and the 5G core network functions on application initiation and UE mobility seems to be missing at this point of time. This paper intends to dig into some of these interworking issues and explains the interactions between the participating entities during the complete application lifecycle.
