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AI Platforms – A Comprehensive Guide

A comprehensive guide compiled to introduce readers to AI platforms, their types, and benefits. A concluding section to discuss AI platform selection strategy with Attri’s Best of Breed approach to build AI platforms. 

Don’t you think that this century is really fortunate? In my opinion, the answer is yes; we witnessed technological transformations and their miracles that created substantial changes in our lifestyle. While talking about these life-changing technological revolutions, AI or artificial intelligence deserves a front seat due to its incredible contribution and capabilities. Now everyone knows AI has limitless potential simply from creating funny faces in mobile to taking informed and intelligent business decisions. In the last 50 years, we have progressed by leaps and bounds to give machines the ability to understand, help and mimic us.

Artificial intelligence enables machines to imitate human intelligence across a variety of domains ranging from problem-solving and reasoning to General Intelligence and in-depth knowledge representation. With tremendous progress in AI, another enabler came into existence and received attention—AI platforms. AI-platform is a layer that integrates all the tools and processes required to build, deploy and monitor ML models. In this article, we shall go through the various aspects of AI platforms covering a range of topics like AI Platform types, the benefits such platforms entail, selection strategy in detail as well as a brief look into Attri’s industry contribution with an Open AI Platform.

Diving Deeper With AI Platforms

The AI Platform acts as a layer over your current AI infrastructure and integrates all the tools and processes required to develop ML models. It provides you the flexibility to integrate all your ML models under a single roof. With this flexibility, you can create and deploy several ML models over the platform. Further, you can even monitor these models to confirm that they are serving their intended purpose. AI platform makes your AI adoption easy by attaining the following requirements–

  • Use of vast data to develop ML solutions.
  • Ensure transparency and reproducibility within a project
  • Accelerate collaboration and governance within teams
  • Ensure scalability for ever-growing machine learning demands

An ideal AI platform should ensure the following features for better addressing different challenges.

  • Seamless access control: Ensure robust access control to team members in order to conquer the challenge of centralized data access with AI projects.
  • Excellent monitoring: Integrate top-notch observability practices while developing ML models.
  • Data and technology-agnostic integration: Seamless experience to enterprises with infrastructure set up responsibility handed over to platform providers
  • All-inclusive Platform: Single platform to facilitate all underlying tasks from data preparation to model deployment
  • Continuous Improvement: Ability to produce and deploy models as a reproducible package and thereby integrate changes with models that are already in production
  • Rapid Processing: Faster data preparation and powerful visual interfaces

AI Platform Classification

With loads of AI platform providers available in the market, AI platform classification becomes a tough job, as it requires thinking separately on each platform’s offerings, its features, and cost factors. Also, you need to check whether AI solutions are open source AI platforms or proprietary offerings.

We have decided to present an AI platform classification based on its striking features and offerings. With this, we have classified AI platforms across three main classes—

  • AI cloud-based platforms
  • AI conversational platforms
  • No code AI platforms

Cloud based AI Platforms

All major cloud providers offer cloud-based AI platforms to boost businesses with AI capabilities. With cloud AI platforms, enterprises can leverage cloud providers’ matchless technical expertise to overcome affordability and data requirement challenges associated with AI implementation. Cloud-based AI offerings benefit businesses with economic AI solutions, defined and pre-packaged services, lower risks, and modern technology.

Amazon Web Services

AWS offers a comprehensive set of AI solutions to conquer major hurdles in the AI adoption journey of businesses. AWS has been recognized as the topmost cloud AI partner with its broad capable portfolio. AWS pre-trained models cater to diverse use cases like forecasting, recommendations, computer vision, language interpretation, customer engagement, and safety for deploying ML models at scale. Amazon also provides text analytics, NLP, chatbots, and document analysis solutions. Fully managed AWS packages amplify your experience with minimum resource requirements and wizard-based friendly model development experience. Hence, AWS is one of the top cloud AI partners that cater to your AI adoption needs.

Google cloud

 The Google Cloud Platform (GCP) is a Google offering for cloud-driven computing services devised to support multiple use cases such as hosting containerized applications, massive-scale data analytics platforms, and even applying ML and AI for business use cases. Google AI Platform is a Google Cloud offering that helps build, deploy and manage machine learning models in the cloud.

Google leverages enterprise AI experience through its consumer-facing products. Google helps improve customer satisfaction through Contact Center AI. Google offering DialogFlow CX is used to create advanced chatbots that handle customer messaging, response, and voice recognition. Digiflow is applied to create virtual agents for messaging services, mobile apps, and IoT devices.

Google’s Cloud Vision API is beneficial to recognize objects, logos, and landmarks within content or images. Google provides Natural Language API to bring more clarity in content classification, entities, syntax, and sentiments. Further, Google speech API helps in converting audio to text and recognizing 110 languages.

Google’s Cloud ML services facilitate better decision-making with end-to-end ML solutions. Google offers an all-inclusive ML development platform that enables effective decision-making backed by explainable AI, continuous evaluation, data labeling, pipelines, training, and what-if tool. This platform is based on the TensorFlow framework and it enables building predictive models for various scenarios.

Kubeflow is a Cloud-Native and open-source platform that helps you build portable ML pipelines that can be executed on-premises or on the cloud. With this, you can access Google technologies like TPUs, TensorFlow, and TFX tools as you deploy your ML models in production.

For expert ML developers, Google provides an Open Source AI platform with TensorFlow models that are trained for various scenarios. It offers an excellent prediction service using trained models.

Microsoft Azure

Similar to Amazon Web Services, Microsoft Azure ML capabilities are based on its real-time and live applications. Azure provides superior machine learning capabilities to develop, train, and deploy machine learning models through Azure Machine Learning, Azure Databricks, and ONNX.

  • Azure Machine Learning

A Python-based ML service to facilitate automated machine learning.

  • ONNX

An open-source model format enables machine learning through various frameworks and hardware platforms of the user’s choice.

  • Azure Cognitive Search

Formerly known as Azure Search,this is the only cloud search service that allows built-in AI capabilities to explore content effectively at scale. Microsoft empowers the user with cognitive search services like text analytics, translation, document analytics, custom vision, and Azure Machine Learning solutions.

IBM Cloud

IBM has brought Watson studio a data analysis application to accelerate innovation and ML-centric practices in business.  IBM Cloud AI Platform offers 170 services with more emphasis on data-speech conversions and analytics. Watson Studio offers an all-inclusive suite to work with data and train, build and deploy ML models.

An innovative giant IBM also brought AI based learning platform recently to aid academic stakeholder like students, researchers and teachers.

AI Conversational Platforms

Conversational AI opens new doors for automated conversations between an enterprise and its customers. These conversations include messaging or voice-based communication platforms to enable text or audio-based conversation.

Conversational platforms leverage your customer experience with a range of applications such as follow-up, guidance, or the resolution of customer queries and round-the-clock support. These platforms are beneficial to drive more leads, increase conversions by cross-selling and upselling, promotional efforts, customer research, queries resolution and customer feedback handling, etc.

AI technology helps systems to mimic human conversations to a certain level and with great accuracy. An AI offering- Natural Language processing is used to shape these conversations by understanding intent, text, speech, and languages.

Intelligent Virtual Assistants

The intelligent virtual assistants represent an advanced level of Conversational AI and their discussion is incomplete without a mention to Siri and Alexa. Most popular intelligent virtual assistants include Siri by Apple, Alexa by Amazon, Google Assistant, and Bixby by Samsung. While Alexa performs as a voice assistant for the home, Siri and Bixby stand as mobile assistants with numerous operations support like navigation, text-to-speech, response to weather, quick reply, and address search.

SAP Conversational AI

SAP Conversational AI is one of the leading conversational AI platforms. With its friendly UI and multiple versioning, it offers a better experience of mimicking human conversations. SAP Conversational AI Platform uses NLP to facilitate developing chatbot that works more humanely and serves your customers 24*7. Its striking features include—

  • Simple integration
  • NLP capabilities
  • Analytics tools to help you
  • Multi-language support

Clinc

A powerful self-learning Conversational AI Platform enriched with NLP capabilities and machine learning. It secures top position in the Conversational AI Platform list due to its learning from previous conversations and improving responses over time. Its feature set include—

  • No technical expertise required
  • Self-learning abilities
  • NLP capabilities

Kore.ai

An enterprise-grade Conversational AI Platform to cater to your consumer as well as staff needs. It helps to build a virtual chatbot for any suitable platform without compromising the safety and security standards. Its major features cover—

  • The high degree of customization for chatbots
  • Comprehensive analytics with FAQs and alerts
  • Simple integration with ML models and channels
  • Flexible deployment
  • Supported with a multi-pronged NLP engine

Mindmeld

It is an excellent option as a Deep-Domain Conversational AI Platform with NLP capabilities. It can be used for both text-based and voice-based virtual assistants. This platform effectively caters to multiple industries and their numerous use cases. Check its striking features list—

  • Open-source platform
  • NLP capabilities
  • Supports discovering on-demand video or music
  • Quick chat-based transactions

No Code AI Platforms

As discussed above, AI platform classification necessitates platform considerations from various perspectives. We are introducing another category of AI platforms—No Code AI Platforms. The motivation behind introducing these platforms is to encourage enterprise AI adoption while keeping AI implementation costs low and minimizing dependencies on skilled professionals. Many IT giants are now offering no-code AI Platforms to enterprises for their AI adoption.

Google ML Kit

Google ML Kit comes with Android and iOS and it facilitates the integration of functions with lesser codes or with minimum knowledge of machine learning algorithms. This open source AI Platform supports different features such as text recognition, face detection, and landmark recognition.

RapidMiner Studio

RapidMiner Studio enables powerful data analytics with drag and drop features. Rapidminer Studio allows easy integration with databases, warehouses, social media for easy data access by authorized persons.

ML Platform Selection Strategy

Having discussed so many types of ML platforms, their features, and offerings, the next question is–how to select the best ML Platform for an enterprise AI adoption. Well, to answer this Million-Dollar question, we need to consider a few key aspects, such as

  • Who will use and benefit from the AI Platform? It is required to find out AI platform users here, the data science team, analytics team, developers, and how the platform will benefit each stakeholder.
  • The next aspect is to explore the skill levels of AI platform users, are they competent to handle ML development and analytics requirements with years of experience
  • Proficiency of users with programming languages
  • The next point in finalizing the AI platform strategy is to conclude code-first or code-free approaches to streamline AI workflows. This aspect can be studied by thinking about different attributes such as data preparation ease, feature engineering automation, ML algorithms, Model Deployment ease, and platform integration aspects.

Once you come up with answers to these queries, you will be able to finalize the best AI Platform Selection strategy for your enterprise. It can be a unique cloud platform, or even it can be a hybrid solution with a “best-of-breed” approach.

All-in-one platform strategy involves getting one end-to-end platform for the entire AI project lifecycle from raw data prep to ETL to building and operationalizing models followed by monitoring and governance of systems.

The best-of-breed approach allows using the preferred and custom tools for each phase of the lifecycle and aligning these tools together to build a customized platform solution for AI adoption.

This approach offers an excellent AI platform solution for organizations looking for flexible, inexpensive, change-oriented AI solutions and having a DIY spirit. With this mix-and-match approach, you can combine APIs offered by different cloud platforms and deliver AI solutions that cater to your AI use cases. Organizations using the best-of-breed approach are more comfortable with technology shifts with their abilities to use, adopt and swap out tools as requirement changes.

Business Process AI Transformation Simplified With Attri’s Open AI Platform

At Attri, we provide AI platform solutions to diverse industry verticals. With our flagship Open AI Platform, we heighten your AI adoption experience with a rich array of platform features like—

  • Customizable best-of-breed architecture
  • Utilize existing infrastructure
  • AI as a platform solution
  • Reduced effort in migrating to a new technology
  • Centralized Monitoring and Governance
  • Explainable and Responsible AI

We help you achieve your business process transformation goals with our unique AI offerings such as Open AI Platform  and Open AI solutions.

Our AI platform assures multiple benefits to your enterprise while keeping AI adoptions costs low and ensuring faster AI implementations. We can summarize the benefits of Attri Open AI Platform as under–

No efforts in reinventing complete AI suites

Attri’s AI Platform integrates multiple AI services and eliminates the need for reinventing complete AI suites. The platform delights enterprises with scalability, the ability to reuse current infrastructure, and customizable architecture.

Accelerated Go To Market

Attri’s Open AI Platform ensures accelerated GTM with a sincere approach to testing, reviewing, and finalizing reference templates for different industries.

No vendor lock-in

With Open AI Platform, we bring client-friendly policies such as no vendor lock-in and flexibility to choose their preferred tools and technology.

High reliability

We keep our AI Platform highly reliable with a comprehensive testing approach. We also meet the growing requirements of enterprises by ensuring high scalability with our open AI platform.

Get connected with us for your enterprise AI adoption requirements.

Know more about our Open AI Platform…

What Is Data Lake Architecture?

The volume of information produced by everyone in the world is growing exponentially. To put it in perspective, it’s estimated that by 2023 the big data analytics market will reach $103 billion.

Finding probable solutions for storing big data is a challenge. It’s no easy task to hold enormous amounts of information, clean it and transform it into understandable subsets — it’s best to take one step at a time.

Some reasons why companies access their big data is to:

  • Improve their consumer experience
  • Draw conclusions and make data-driven decisions
  • Identify potential problems
  • Create innovative products

There are ways to help define big data. Combining its characteristics with storage management methods help experts make their clients’ information digestible and understandable. Cue data lakes, which are repositories for big data in its native form.

Think of an actual lake with multiple water sources around the perimeter flowing into it. Picture these as three types of data: structured, semi-structured and unstructured. All this information can remain in a data lake and be accessed in its raw form at any time, making it an attractive storage method.

Here’s how data lakes are created, some of their components and how to avoid common pitfalls.

Creating a Data Lake

One benefit of creating and implementing a data lake is that structuring becomes much more manageable.  Pulling necessary information from a lake allows analysts to compare and contrast data and communicate any connections between datasets to their client.

There are four steps to follow when setting up a data lake:

  1. Choosing a software solution: Microsoft, Amazon and Google are cloud vendors that allow developers to create data lakes without using servers.
  2. Identifying where data is sourced: Where is your information coming from? Once sources are identified, determine how your data will be cleaned or transformed.
  3. Defining process and automation: It’s vital to outline how information should be processed once the data lake ingests it. This creates consistency for businesses.
  4. Establishing retrieval governance: Choosing who has access to what types of information is crucial for companies with multiple locations and departments. It helps with overall organization. Data scientists, for this reason, primarily access data lakes.

The next step would be to determine the extract, transform and load (ETL) process. ETL creates visual interpretations of data to provide context to businesses. When information from a data lake is sent to a warehouse, it can be analyzed.

Components of a Data Lake

Here is what happens to information once a data lake is created:

  • Collection: Data comes in from various sources.
  • Ingestion: Data is processed using management software.
  • Blending: Data is combined from multiple sources.
  • Transformation: Data is analyzed and made sense of.
  • Publication: Data can be used to drive business decisions.

There are other aspects of a data lake to keep in mind. These are the critical components that help provide business solutions:

  • Security: Data lakes require security to protect information — they do not have built-in safety measures.
  • Governance: Determine who can check on the quality of data and perform measurements.
  • Metadata: This provides information about other data to improve understanding.
  • Stewardship: Choose one or more employees to take on the responsibility of managing data.
  • Monitoring: Employ other software to perform the ETL process.

Big data lends itself to incorporating multiple processes to make it usable for companies. The volume of information one company produces is massive — to manage it, experts need to consider these components and steps when building a data lake.

What to Avoid When Using Data Lakes

The last thing people want for their data lake is to see it turn into a swamp. When big data is processed incorrectly, its value decreases, making it useless to the business sourcing it.

The first step in avoiding a common pitfall is to consider the sustainability of the data lake. Planning processes are necessary to ensure it’s secure, and governing and regulating incoming information will allow for long-term use.

A lack of security causes another problem that can arise in data lakes. Safety measures must be implemented. Because enterprises will build data lakes for different purposes, it’s easy for information to become unorganized and vulnerable to hacking. With security, the likelihood of data breaches decreases, and the quality of data remains high.

The most important thing to remember about data lakes is the planning stage. Without proper preparation, they tend to be overwhelming due to their size and complexity. Taking the time and care to establish the processes ahead of time is vital.

Using Data Lake Architecture for Business

Data lakes store massive amounts of information to be used later on to create subsets, analyze metadata and more. Their advantages allow businesses to be flexible, save money and have access to raw information at all times.

How Microsoft Azure Is Impacting Financial Companies

Microsoft Azure has taken a large chunk of the cloud marketplace, transforming companies with the speed and security of the cloud. Microsoft has over the years used Azure to cushion companies against risk, deal with fraud and differentiate their customer experience. 

With Microsoft Cloud App Security, customers experience 75% automatic threat elimination because of increased visibility and automated threat protection. With all these and more amazing benefits of using Azure, its market share is bound to increase even more over the coming years.

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Financial companies have not been left behind by the Azure bandwagon. The financial industry is using Microsoft Azure to enhance its core functionsinvest money by making informed decisions, and minimize risk while maximizing returns. 

Azure facilitates these core functions by helping with the storage of huge amounts of data—  some dating back to decades ago—, data retrieval and data security. 

It also helps financial companies to keep up with regulatory compliance.

Microsoft Azure is not the only cloud services provider. But here’s why it is the most outstanding when it comes to helping financial companies achieve their business goals.

Azure Offers Hybrid and Multi-Cloud Computing for Financial Companies

The financial services industry is extremely dynamic. Organizations offering financial services have to constantly test the market and come up with new and innovative products and services. 

They are also often under pressure to extend their services across borders. Remember they have to do all of this while at the same time managing their existing customers, containing their risk, and dealing with fraud.

Financial regulations also keep changing. As financial companies increasingly embrace new technology for their services— including intelligent cloud computing— and they have to comply with industry regulations. They cannot afford to leave loopholes as they take on their journey with the cloud.

The financial services industry is highly competitive and keeps up with modernity. These companies have had to resort to the dynamic hybrid, multi-cloud computing, and public cloud strategies to keep up with the trend.

This is how a hybrid cloud model worksit enables existing on-premises applications to be extended through a connection to the public cloud. 

This allows financial companies to enjoy the speed, elasticity, and scale of the public cloud without necessarily having to remodel their entire applications. These organizations are afforded the flexibility of deciding what parts of their application remains in an existing data center and which one resides in the cloud.

Cloud computing with Azure allows financial organizations to operate more efficiently by providing end-to-end protection to information, allowing the digitization of financial services, and providing data security. 

Data security is particularly important to financial firms because they are often targeted by fraudsters and cyber threats. They, therefore, need to protect crucial information which they achieve by authenticating their data centers using Azure.

Here’s why financial companies cannot think of doing without Azure’s hybrid cloud computing even for just a day.

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Photo by Windows on Unsplash

  • The ability to expand their geographic reach

Azure enables financial companies to establish data centers in new locations to meet globally growing demand. This allows them to open and explore new markets. They can then use Azure DevOps pipelines to maintain their data factories and keep everything consistent.

  • Consistent Infrastructure management

The hybrid cloud model promotes a consistent approach to infrastructure management across all locations, whether it is on-premises, public cloud, or the edge.

  • Increased Elasticity

Financial firms and banks utilizing Azure services can respond with great agility to transactional changes or changes in demand by provisioning or de-provisioning as the situation at hand demands. 

In cases where the organization requires high computation such as complex risk modeling, a hybrid strategy allows it to expand its capacity beyond its data center without overwhelming its servers.

  • Flexibility

A hybrid strategy allows financial organizations to choose cloud services that fall within their budget, match their needs, and suit their features.

  • Data security and enhanced regulatory compliance

Hybrid and multi-cloud strategies are a superb alternative for strictly on-premises strategies when one considers resiliency, data portability, and data security.

  • Reduces CapEx Expenses

Managing on-premises infrastructure is expensive. Financial companies utilizing Azure do not need to spend large amounts of money setting them up and managing them. 

With the increased elasticity of the hybrid system, financial organizations only pay for the resources they actually use, at a relatively lower cost.

Financial Organizations Have Access to an Analytics Platform

As we mentioned earlier, financial companies have the core function of making financial decisions in order to invest money and gain maximum returns at the least possible risk. 

Having been entrusted with their customers’ assets, the best way to ensure success in making profits is by using an analytics system.

Getting the form of analytics that helps with solving this investment problem is the kind of headache that does not go away by taking a tablet of ibuprofen and a glass of waterintegrating data is not an easy task. Besides, building a custom analytics solution from scratch is quite expensive.

Luckily for financial companies, Azure has a dedicated analytics platform for the financial services industry. It is custom-made just for these types of organizations. 

Their system is quite intuitive and easy to use. Companies not only get to save the resources they would have otherwise used to build a custom solution, but they get to learn about their investment risks and get instant results at cloud speed. 

They can mitigate against negatively impactful market occurrences and gain profits even when operating in adverse market conditions.

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Image by Headway on Unsplash

Financial Companies Get Advanced Data Management

Good analytics goes hand-in-hand with a great data management system. Financial companies need to have good data, create an organized data warehouse, and have a secure data storage system.

In addition to storing your data, Microsoft Azure ensures your storage can be optimized to support advanced applications, for example, machine learning and forecasting. 

Azure even allows you to compress and store documents for long periods of time when you write the data to Microsoft Azure Blob Storage. These documents can be retrieved anytime when the need arises for auditors’, regulators’, and lawyers’ perusal. 

Conclusion

Microsoft has over time managed to gain the trust of many industries, the financial services industry inclusive. Using its cloud computing giant, Azure, it has empowered these companies to carry out their functions efficiently and at the lowest cost and risk possible.

Azure’s hybrid cloud computing strategy has made financial operations flexible, opened doors for financial companies to establish their services in multiple locations, and provided them with consistent infrastructure management, among many other benefits.

With their futuristic model and commitment to growth, it’s only prudent to assume that Microsoft Azure will continue carrying the mantle as the best cloud services provider in the financial services industry.

Interview: Does Business Intelligence benefit from Cloud Data Warehousing?

Interview with Ross Perez, Senior Director, Marketing EMEA at Snowflake

Read this article in German:
“Profitiert Business Intelligence vom Data Warehouse in der Cloud?”

Does Business Intelligence benefit from Cloud Data Warehousing?

Ross Perez is the Senior Director, Marketing EMEA at Snowflake. He leads the Snowflake marketing team in EMEA and is charged with starting the discussion about analytics, data, and cloud data warehousing across EMEA. Before Snowflake, Ross was a product marketer at Tableau Software where he founded the Iron Viz Championship, the world’s largest and longest running data visualization competition.

Data Science Blog: Ross, Business Intelligence (BI) is not really a new trend. In 2019/2020, making data available for the whole company should not be a big thing anymore. Would you agree?

BI is definitely an old trend, reporting has been around for 50 years. People are accustomed to seeing statistics and data for the company at large, and even their business units. However, using BI to deliver analytics to everyone in the organization and encouraging them to make decisions based on data for their specific area is relatively new. In a lot of the companies Snowflake works with, there is a huge new group of people who have recently received access to self-service BI and visualization tools like Tableau, Looker and Sigma, and they are just starting to find answers to their questions.

Data Science Blog: Up until today, BI was just about delivering dashboards for reporting to the business. The data warehouse (DWH) was something like the backend. Today we have increased demand for data transparency. How should companies deal with this demand?

Because more people in more departments are wanting access to data more frequently, the demand on backend systems like the data warehouse is skyrocketing. In many cases, companies have data warehouses that weren’t built to cope with this concurrent demand and that means that the experience is slow. End users have to wait a long time for their reports. That is where Snowflake comes in: since we can use the power of the cloud to spin up resources on demand, we can serve any number of concurrent users. Snowflake can also house unlimited amounts of data, of both structured and semi-structured formats.

Data Science Blog: Would you say the DWH is the key driver for becoming a data-driven organization? What else should be considered here?

Absolutely. Without having all of your data in a single, highly elastic, and flexible data warehouse, it can be a huge challenge to actually deliver insight to people in the organization.

Data Science Blog: So much for the theory, now let’s talk about specific use cases. In general, it matters a lot whether you are storing and analyzing e.g. financial data or machine data. What do we have to consider for both purposes?

Financial data and machine data do look very different, and often come in different formats. For instance, financial data is often in a standard relational format. Data like this needs to be able to be easily queried with standard SQL, something that many Hadoop and noSQL tools were unable to provide. Luckily, Snowflake is an ansi-standard SQL data warehouse so it can be used with this type of data quite seamlessly.

On the other hand, machine data is often semi-structured or even completely unstructured. This type of data is becoming significantly more common with the rise of IoT, but traditional data warehouses were very bad at dealing with it since they were optimized for relational data. Semi-structured data like JSON, Avro, XML, Orc and Parquet can be loaded into Snowflake for analysis quite seamlessly in its native format. This is important, because you don’t want to have to flatten the data to get any use from it.

Both types of data are important, and Snowflake is really the first data warehouse that can work with them both seamlessly.

Data Science Blog: Back to the common business use case: Creating sales or purchase reports for the business managers, based on data from ERP-systems such as Microsoft or SAP. Which architecture for the DWH could be the right one? How many and which database layers do you see as necessary?

The type of report largely does not matter, because in all cases you want a data warehouse that can support all of your data and serve all of your users. Ideally, you also want to be able to turn it off and on depending on demand. That means that you need a cloud-based architecture… and specifically Snowflake’s innovative architecture that separates storage and compute, making it possible to pay for exactly what you use.

Data Science Blog: Where would you implement the main part of the business logic for the report? In the DWH or in the reporting tool? Does it matter which reporting tool we choose?

The great thing is that you can choose either. Snowflake, as an ansi-Standard SQL data warehouse, can support a high degree of data modeling and business logic. But you can also utilize partners like Looker and Sigma who specialize in data modeling for BI. We think it’s best that the customer chooses what is right for them.

Data Science Blog: Snowflake enables organizations to store and manage their data in the cloud. Does it mean companies lose control over their storage and data management?

Customers have complete control over their data, and in fact Snowflake cannot see, alter or change any aspect of their data. The benefit of a cloud solution is that customers don’t have to manage the infrastructure or the tuning – they decide how they want to store and analyze their data and Snowflake takes care of the rest.

Data Science Blog: How big is the effort for smaller and medium sized companies to set up a DWH in the cloud? Does this have to be an expensive long-term project in every case?

The nice thing about Snowflake is that you can get started with a free trial in a few minutes. Now, moving from a traditional data warehouse to Snowflake can take some time, depending on the legacy technology that you are using. But Snowflake itself is quite easy to set up and very much compatible with historical tools making it relatively easy to move over.