How can cloud data warehouse tools help organizations scale their data warehousing capabilities?

In this era of cloud where everyone choosing cloud over on prem, We need to know why ?? One of the main reason they come up with unlimited storage and allow us to scale( Horizontal or Vertical) our compute power depending on our needs. Cloud data warehouse tools use advanced technologies, such as columnar storage, compression, caching, clustering &automatic partitioning. If I consider Snowflake, core architecture is "Multi cluster Shared data". Which mean centralized and elastic storage, Multiple MPP, Independent compute clusters which helps us to scale as per our need. #snowflake #clouddatawarehouse

Before understanding cloud warehouse let’s take a look at challenges of Traditional warehouse ecosystem on premise. It lacked easy scaling , agility, heavy operational maintenance. In order to retrieve any backdated deleted data it take mammoth effort to search through transactions logs . It has to be modelled diligently for efficient queries execution. Cloud warehouse like snowflake , redshift etc gives much more flexibility and has taken pain point of different user persona Into consideration.. Now I can segregate computes based on requirements . Dw gets into sleep mode when not in use hence savings . With Time travel I can go back to exact back dates version of data . Efficient predicate push makes sure to run Compute on storage etc.

I feel cloud data warehouses are best of both the world's on-prem and cloud. It's easier, reliable and more flexible option for data warehousing in modern edge of AI & ML. most of the cloud solutions have lot of in-built capabilities for your end to end data journey like ingestion, cleaning, formatting, transformation, harmonisation, validation, quality, feature exploration, model building, training and things like BigQuery ML help you deliver even the ML use-cases out of the box, this also comes as at your will and with less administration overhead with complete state of the art options for data governance and cataloging.

Cloud Data WH Tool, but still client are not ready to adopt Cloud Data WH due to their zone restriction and security concern. First need to educate our self to use Cloud Data WH with proper security and compliance. Also Data Migration from on perm to cloud should be in proper way includes security and compliance.

Data warehouse may become a commodity in a few years. With powerful real time data processing and generative AI it might be possible to analyze and mine insights real time with no need for a data warehouse as such. May not be valid for all the industries and there would be need for data warehouses or data lakes still for doing regular reporting etc.

Cloud data warehouse tools help organizations scale data warehousing capabilities by offering scalable resources, cost-efficiency, high performance, flexibility for various data types, and global accessibility. They also provide robust security, easy integration with other cloud services, automatic updates, data recovery, and built-in analytics services, making it easier for organizations to manage and analyze their data efficiently.

Cloud data warehouse tools offer elastic scalability enabling organisations to store and process large amounts of data on-demand. They also provide 1. Seamless integration with various data sources 2. Support parallel processing to handle large volume of data 3. Provides ability to analyse data in realtime making it easier to extract insights 4. Majorly, they reduce the need for upfront hardware investments & maintenance allowing organisations quickly adapt to the data needs and optimise costs.

All the on demand compute and storage availability in the cloud data warehouse systems are there to scale up and/or down as you see fit. This is a flexibility and scalability that was never available to on prem solutions

one of the unique features about cloud data warehouse is it's low or zero upfront costs. All important cloud dwh like Snowflake has 2 ways of payment. 1. Pay-as-you-go : This is very comfortable option for many organizations who wish to perform large scale PoCs(which can go for couple of months) to test the features and compatibility of the DWH into their existing eco-system. 2. Capacity purchases: An enterprise can make upfront capacity purchase by negotiating with the DWH vendor. This can give stronger negotiation power(& discounts) to the organizations and long term commitment to the vendor. In both the cases organizations can scale up quickly their storage or compute with out any limits.

Cloud D/W are built on Cloud platforms which leverages the standard offerings of IaaS, PaaS & SaaS. While each offering has its own benefit, the cloud data warehouse tools deployed / enabled take away all the headache of native integration, installation, configuration with nearly zero administration cost beyond standard cloud benefits. Nevertheless, you are always up to speed as far as the product version / stability is concerned from the patches perspective. Scalability as a factor of vertical vs. horizontal vs. hybrid are choices by design rather than by need.

Performance is a factor of multiple aspects starting from architecture, design, usage, concurrency and the horse power behind the scene in terms of H/W. Cloud data warehouses leverages cloud ecosystems to ensure that you have enough horse power to run the processes if configured correctly for a deployment. Auto-tune and zero admin features of latest cloud D/W platforms ensures that the bottlenecks around performance are tackled real-time rather than being reactive. Networking features like fault tolerance and load balancing are icing on the care to ensure seamless availability with distributed load for peak performance.

One of the biggest advantages of cloud data warehouses is their ability to separate storage and compute power. Although on-premise tools also offer features like columnar storage and support for structured/unstructured data, the problem is that you cannot scale your compute independent of storage. This means that you end up paying for storage resources that you are not actually using while trying to scale up compute. On the other hand, with cloud data warehouses, you can schedule your compute resources to scale up during peak usage times, such as month-end, and only pay for what you actually use.

We can perform machine learning and analytics but when the size of data grows the performance decreases. You cannot just put data in data warehouse without using it. When we go back to 5 years of transactional data to do analytics in SQL or extract it and do analytics or ML in any programming language then the performance is impacted. It's time to provide in build analytics function with twicks and ML models with customisation in data warehouse.

Performance can have different meanings, but in general it's measured by time taken by a large query to run. Driven by two factors, the secret sauce of how data is stored and read, and how much processing power is available. Btw, all cloud providers benchmark their speed against each other and promote their speeds. Its become less of a differentiator these days they are mostly in similar ranges. In general, data warehouses use columnar storage method, and its optimized for running queries. So they are all fast. Cloud providers offer different sized engines e.g. a Corolla's 1.8L gas engine to 16L truck engine. If you run a large query and want results in seconds, bump up the engine size (and expect to pay more)!

Cloud data warehouse tools are performance champs. They use advanced tech like columnar storage, compression, caching, indexing, partitioning, and optimization to make data warehouses super speedy and accurate. What's even cooler is that they support all sorts of data types, whether it's organized, kinda organized, or all over the place. You can even do fancy stuff like machine learning, artificial intelligence, and geospatial analysis on your data. Plus, they have some tricks up their sleeves, like auto-scaling, load balancing, and fault tolerance, to keep your data warehouse up and running smoothly, ensuring top-notch performance.

Las inversiones que hacen los proveedores de nube no se pueden comparar con las inversiones de las compa?ías cuyo core business no es la tecnología, a través de la nube estas inversiones son usadas en favor de los usuarios. Adicionalmente la capacidad y servicios de seguridad hacen que sean parte de la diferencia entre proveedores de nube

Cloud data warehouse tools can offer several advantages when compare to traditional on-premises data warehouses below are some major advantages 1-->Scalability 2-->Availability 3-->cost effective 4-->Easy to maintain 5--->Performance

Security is a broad term. Most cloud warehouses will allow you to do the following 1) Limit access to just authorized users to even attempt a login (whitelisting/vpc setup) 2) Multi factor sign in, out of the box or via identity management tools e.g. Onelogin, Okta etc. 3) Once logged in, being able to access just the datasets they have privileges too (this has existed for many decades, nothing new here). In addition to DB, Schemas, Table, Views, there is also row level access management 4) Bullets 1 to 3 have existed for a while. The real innovation is in two areas a) Easy masking of PII info b) Secure data sharing 5)Governance, especially with data powering ML and AI use cases is still in early stages, more to come in 2024.

Cloud data warehouse tools offer robust security features to protect sensitive data. Here are some ways they contribute to data security: Encryption: This ensures that data is securely stored and transferred between the user and the cloud data warehouse. Access Controls: This helps prevent unauthorized users from accessing sensitive information. Authentication: This adds an extra layer of security by requiring users to provide multiple forms of identification, such as passwords and verification codes. Auditing and Monitoring: This allows organizations to detect and respond to any suspicious or unauthorized activities promptly. Compliance Certifications: This can include standards like SOC 2, HIPAA, or GDPR. .

The last great thing about cloud data warehouse tools is that they make organizations super flexible and quick to respond to changing business and market needs. They're easy to set up and use, and you don't need to be an IT expert. You can also bring in and analyze data from all over the place, like databases, applications, files, streams, and APIs, using different tools like SQL, Python, R, or BI tools. What's even more fantastic is that these tools support something called a data lakehouse, which is like a mix of the best bits from data lakes and data warehouses, like being flexible, stretchy, and reliable. With this, you can work with both raw and polished data in one place, without messing up the data's quality or rules.

Integrating other tools with data warehouse is good idea but in case of huge data , the performance of tool is also hampered. If we get all the analytics/ML functionality within datawarehouse without hampering the performance then it is a game changer. Do not create copy of data from datawarehouse to some other BI tool.

Cloud data warehouse tools contribute significantly to organizational agility by offering the following benefits: Rapid Deployment: Scalability: Flexibility in Data Processing: Collaboration and Remote Access: Integration with Other Cloud Services: Automatic Updates and Maintenance: Cost Optimization:

Cloud has made data warehouses very easy to implement with data lake architecture we can analyse big data very easily and quickly.

??Cloud data warehouse tools help people create, organize, & ask questions about big collections of data. ?? Cloud data warehouse tools can help save money because you don't need to buy & manage lots of physical computers. ?? Cloud data warehouse tools are like special computer programs. ?? Cloud data warehouse tools make it possible for companies to grow & change without much trouble. ?? They make sure the information is not lost & is always available. ?? These tools store and organize lots of information on the internet. ?? They make it easy to use the information for your work. ?? They keep the information private & protected. ?? These tools make it simple for many people to work on the same data at the same time.

Here's another interesting fact about cloud data warehouse tools: they've revolutionized the way organizations handle data. In the past, dealing with massive datasets and performing complex analyses often meant investing heavily in infrastructure, hiring specialized IT teams, and facing long implementation timelines. However, with the advent of cloud data warehouse tools, even small and medium-sized businesses can leverage the power of big data. They've democratized data analytics, making it accessible to a wider range of users, from startups to established enterprises. This accessibility has opened up new opportunities for innovation and informed decision-making, leveling the playing field in the data-driven world.