#computering consulting

[beepboopappreciation]

Finally finished putting on all my laptop stickers!! Special shout-out to @colliholly and @dookins for AMAZING sticker designs :]

[capitalism-and-analytics]

[appendingfic]

Every time I read about smart fridges I'm like, Dune had a point with the Butlerian Jihad

[especiallyhaytham]

I ironically-unironically love David Cage/Quantic Dream games. They're so unintentionally bad, so passionately talentless. I delight in his weird little god-complex unrealities where everything is tainted by egomania and narcissism. The man is delusional and needs to be put down.

[retrocgads]

UK 1987

[bredforloyalty]

i wonder if it can be managed that i no longer live in that city but i do a research project during the fall semester

[sporadicfrogs]

god I fucking hate corporate buzzword bullshit

[wojakgallery]

Title/Name: Edward Joseph Snowden, better known as ‘Edward Snowden’ or simply ‘Snowden’, born in (1983). Bio: American and naturalized Russian citizen who was a computer intelligence consultant and whistleblower who leaked highly classified information from the National Security Agency in 2013 when he was an employee and subcontractor. Country: USA / Russia Wojak Series: Feels Guy (Variant) Image by: Wojak Gallery Admin Main Tag: Snowden Wojak

[coffeebeansconsulting]

What is Serverless Computing?

Serverless computing is a cloud computing model where the cloud provider manages the infrastructure and automatically provisions resources as needed to execute code. This means that developers don’t have to worry about managing servers, scaling, or infrastructure maintenance. Instead, they can focus on writing code and building applications. Serverless computing is often used for building event-driven applications or microservices, where functions are triggered by events and execute specific tasks.

How Serverless Computing Works

In serverless computing, applications are broken down into small, independent functions that are triggered by specific events. These functions are stateless, meaning they don’t retain information between executions. When an event occurs, the cloud provider automatically provisions the necessary resources and executes the function. Once the function is complete, the resources are de-provisioned, making serverless computing highly scalable and cost-efficient.

Serverless Computing Architecture

The architecture of serverless computing typically involves four components: the client, the API Gateway, the compute service, and the data store. The client sends requests to the API Gateway, which acts as a front-end to the compute service. The compute service executes the functions in response to events and may interact with the data store to retrieve or store data. The API Gateway then returns the results to the client.

Benefits of Serverless Computing

Serverless computing offers several benefits over traditional server-based computing, including:

Reduced costs: Serverless computing allows organizations to pay only for the resources they use, rather than paying for dedicated servers or infrastructure.

Improved scalability: Serverless computing can automatically scale up or down depending on demand, making it highly scalable and efficient.

Reduced maintenance: Since the cloud provider manages the infrastructure, organizations don’t need to worry about maintaining servers or infrastructure.

Faster time to market: Serverless computing allows developers to focus on writing code and building applications, reducing the time to market new products and services.

Drawbacks of Serverless Computing

While serverless computing has several benefits, it also has some drawbacks, including:

Limited control: Since the cloud provider manages the infrastructure, developers have limited control over the environment and resources.

Cold start times: When a function is executed for the first time, it may take longer to start up, leading to slower response times.

Vendor lock-in: Organizations may be tied to a specific cloud provider, making it difficult to switch providers or migrate to a different environment.

Some facts about serverless computing

Serverless computing is often referred to as Functions-as-a-Service (FaaS) because it allows developers to write and deploy individual functions rather than entire applications.

Serverless computing is often used in microservices architectures, where applications are broken down into smaller, independent components that can be developed, deployed, and scaled independently.

Serverless computing can result in significant cost savings for organizations because they only pay for the resources they use. This can be especially beneficial for applications with unpredictable traffic patterns or occasional bursts of computing power.

One of the biggest drawbacks of serverless computing is the “cold start” problem, where a function may take several seconds to start up if it hasn’t been used recently. However, this problem can be mitigated through various optimization techniques.

Serverless computing is often used in event-driven architectures, where functions are triggered by specific events such as user interactions, changes to a database, or changes to a file system. This can make it easier to build highly scalable and efficient applications.

Now, let’s explore some other serverless computing frameworks that can be used in addition to Google Cloud Functions.

AWS Lambda: AWS Lambda is a serverless compute service from Amazon Web Services (AWS). It allows developers to run code in response to events without worrying about managing servers or infrastructure.

Microsoft Azure Functions: Microsoft Azure Functions is a serverless compute service from Microsoft Azure. It allows developers to run code in response to events and supports a wide range of programming languages.

IBM Cloud Functions: IBM Cloud Functions is a serverless compute service from IBM Cloud. It allows developers to run code in response to events and supports a wide range of programming languages.

OpenFaaS: OpenFaaS is an open-source serverless framework that allows developers to run functions on any cloud or on-premises infrastructure.

Apache OpenWhisk: Apache OpenWhisk is an open-source serverless platform that allows developers to run functions in response to events. It supports a wide range of programming languages and can be deployed on any cloud or on-premises infrastructure.

Kubeless: Kubeless is a Kubernetes-native serverless framework that allows developers to run functions on Kubernetes clusters. It supports a wide range of programming languages and can be deployed on any Kubernetes cluster.

IronFunctions: IronFunctions is an open-source serverless platform that allows developers to run functions on any cloud or on-premises infrastructure. It supports a wide range of programming languages and can be deployed on any container orchestrator.

These serverless computing frameworks offer developers a range of options for building and deploying serverless applications. Each framework has its own strengths and weaknesses, so developers should choose the one that best fits their needs.

Real-time examples

Coca-Cola: Coca-Cola uses serverless computing to power its Freestyle soda machines, which allow customers to mix and match different soda flavors. The machines use AWS Lambda functions to process customer requests and make recommendations based on their preferences.

iRobot: iRobot uses serverless computing to power its Roomba robot vacuums, which use computer vision and machine learning to navigate homes and clean floors. The Roomba vacuums use AWS Lambda functions to process data from their sensors and decide where to go next.

Capital One: Capital One uses serverless computing to power its mobile banking app, which allows customers to manage their accounts, transfer money, and pay bills. The app uses AWS Lambda functions to process requests and deliver real-time information to users.

Fender: Fender uses serverless computing to power its Fender Play platform, which provides online guitar lessons to users around the world. The platform uses AWS Lambda functions to process user data and generate personalized lesson plans.

Netflix: Netflix uses serverless computing to power its video encoding and transcoding workflows, which are used to prepare video content for streaming on various devices. The workflows use AWS Lambda functions to process video files and convert them into the appropriate format for each device.

Conclusion

Serverless computing is a powerful and efficient solution for building and deploying applications. It offers several benefits, including reduced costs, improved scalability, reduced maintenance, and faster time to market. However, it also has some drawbacks, including limited control, cold start times, and vendor lock-in. Despite these drawbacks, serverless computing will likely become an increasingly popular solution for building event-driven applications and microservices.

Read more

[deadpoolsbathwater]

currently in the midst of a big dumb bitch moment and we’re taking casualties

[searchingforserendipity25]

Guess who burned their thumb in the dumbest possible way ?

[capitalism-and-analytics]

[cloudatlasinc]

Accelerating transformation with SAP on Azure

Microsoft continues to expand its presence in the cloud by building more data centers globally, with over 61 Azure regions in 140 countries. They are expanding their reach and capabilities to meet all the customer needs. The transition from a cloudless domain like DRDC to the entire cloud platform is possible within no time, and a serverless future awaits. Microsoft gives the platform to build and innovate at a rapid speed. Microsoft is enhancing new capabilities to meet cloud services' demands and needs, from IaaS to PaaS Data, AI, ML, and IoT. There are over 600 services available on Azure with a cloud adoption framework and enterprise-scale landing zone. Many companies look at Microsoft Azure security compliance as a significant migration driver. Microsoft Azure has an extensive list of compliance certifications across the globe. The Microsoft services have several beneficial characteristics; capabilities are broad, deep, and suited to any industry, along with a global network of skilled professionals and partners. Expertise in the Microsoft portfolio includes both technology integration and digital transformation. Accountability for the long term, addressing complex challenges while mitigating risk. Flexibility to engage in the way that works for you with the global reach to satisfy the target business audience.

SAP and Microsoft Azure

SAP and Microsoft bring together the power of industry-specific best practices, reference architectures, and professional services and support to simplify and safeguard your migration to SAP in the cloud and help manage the ongoing business operations now and in the future. SAP and Microsoft have collaborated to design and deliver a seamless, optimized experience to help manage migration and business operations as you move from on-premises editions of SAP solutions to SAP S/4 HANA on Microsoft Azure. It reduces complexity, minimizes costs, and supports end-to-end SAP migration and operations strategy, platform, and services. As a result, one can safeguard the cloud migration with out-of-box functionality and industry-specific best practices while immaculately handling the risk and optimizing the IT environment. Furthermore, the migration assimilates best-in-class technologies from SAP and Microsoft, packed with a unified business cloud platform. 

SAP Deployment Options on Azure

SAP system is deployed on-premises or in Azure. One can deploy different systems into different landscapes either on Azure or on-premises. SAP HANA on Azure large instances intend to host the SAP application layer of SAP systems in Virtual Machines and the related SAP HANA instance on the unit in the 'SAP HANA Azure Large Instance Stamp.' 'A Large Instance Stamp' is a hardware infrastructure stack that is SAP HANA TDI certified and dedicated to running SAP HANA instances within Azure. 'SAP HANA Large Instances' is the official name for the solution in Azure to run HANA instances on SAP HANA TDI certified hardware that gets deployed in ‘Large Instance Stamps’ in different Azure regions. SAP or HANA Large Instances or HLI are physical servers meaning bare metal servers. HLI does not reside in the same data center as Azure services but is in close proximity and connected through high throughput links to satisfy SAP HANA network latency requirements. HLI comes in two flavors- Type 1 and 2. IaaS can install SAP HANA on a virtual machine running on Azure. Running SAP HANA on IaaS supports more Linux versions than HLI. For example, you can install SAP Netweaver on Windows and Linux IaaS Virtual Machines on Azure. SAP HANA can only run on RedHat and SUSE, while NetWeaver can run on windows SQL and Linux.

Azure Virtual Network

Azure Virtual Network or VNET is a core foundation of the infrastructure implementation on Azure. The VNET can be a communication boundary for those resources that need to communicate. You can have multiple VNETs in your subscription. If they weren't connected, we could call them Pierre in Azure wall; there will be no traffic flow in between. They can also share the same IP range. Understanding the requirements and proper setup is essential as changing them later, especially with the running production workloads, could cause downtime. When you provision a VNET, The private blocks must allocate address space. If you plan to connect multiple VNETs, you cannot have an overlapping address space. The IP range should not clash or overlap with the IP addressing in Azure while connecting on-premise to Azure via express route or site-site VPN. Configuring VNET to the IP address space becomes a DHP service. You can configure VNET with the DNS server's IP addresses to resolve services on-premise.VNETS can be split into different subnets and communicate freely with each other. Network security groups or NSGs are the control planes we use to filter traffic. NSGs are stateful but simple firewall rules based on the source and destination IP and ports.

 Azure Virtual Gateway

 For extensive connectivity, you must create a virtual gateway subnet. When you create a virtual gateway, you will get prompted for two options: VPN or Express Route Gateway; with VPN, you cannot connect to the Express Route Circuit. If you choose the Express Route Virtual Gateway, you can combine both.

 There are two types of VPN;

1) The point-to-site VPN is used for testing and gives the lowest throughput.

2) The site-site VPN connection can offer better benefits by bridging networks.

This VPN offers zero support for SLA and uses this connection as a backup for the recommended connection on Azure, called the express route. Express route is a dedicated circuit using hardware installed on your data center, with a constant link to ‘Microsoft Azure Edge’ devices. Express route is inevitable for maintaining the communication between application VNET running in Azure and on-premise systems to HLI servers. The express route is safer and more resilient than VPN as it provides a connection through a single circuit and facilitates second redundancy; this helps route traffic between SAP application servers inside Azure and enables low latency. Furthermore, the fast path allows routine traffic between SAP application servers inside Azure VNET and HLI through an optimized route that bypasses the virtual network gateway and directly hops through edge routers to HLA servers. Therefore, an ultra-performance express route gateway must have a Fast Path feature.

SAP HANA Architecture (VM)

This design gets centered on the SAP HANA backend on the Linux Suse or RedHat distributions. Even though the Linux OS implementation is the same, the vendor licensing differs. It incorporates always-on replication and utilizes synchronous and asynchronous replication to meet the HANA DB requirements. We have also introduced NetApp file share for DFS volumes used by each SAP component using Azure site recovery and building a DR plan for App ASCs and the web dispatches servers. Azure Active directory is used in synchronization with on-premises active directory, as SAP application user authenticates from on-premises to SAP landscape on Azure with Single Sign-On credentials. Azure high-speed express route gateway securely connects on-premises networks to Azure virtual machines and other resources. The request flows into highly available SAP central, SAP ABAP services ASCS and through SAP application servers running on Azure virtual machines. The on-demand request moves from the SAP App server to the SAP HANA server running on a high-performance Azure VM. Primary active and secondary standby servers run on SAP-certified virtual machines with a cluster availability of 99.95 at the OS level. Data replication is handled through HSR in synchronous mode from primary to secondary enabling zero recovery point objective. SAP HANA data is replicated through a disaster recovery VM in another Azure region through the Azure high-speed backbone network and using HSR in asynchronous mode. The disaster recovery VM can be smaller than the production VM to save costs.

SAP systems are network sensitive, so the network system must factor the design decisions into segmenting the VNETs and NSGs. To ensure network reliability, we must use low latency cross-connections with sufficient bandwidth and no packet loss. SAP is very sensitive to these metrics, and you could experience significant issues if traffic suffers latency or packet loss between the application and the SAP system. We can use proximity placement groups called PGS to force the grouping of different VM types into a single Azure data center to optimize the network latency between the different VM types to the best possible.

 Security Considerations

 Security is another core pillar of any design. Role-based Access control (RBAC) gets accessed through the Azure management bay. RBAC is backed up through AD using cloud-only synchronized identities. Azure AD can back up the RBAC through cloud-only or synchronized identities. RBAC will tie in those cloud or sync identities to Azure tenants, where you can give personal access to Azure for operational purposes. Network security groups are vital for securing the network traffic both within and outside the network environment. The NSGs are stateful firewalls that preserve session information. You can have a single NSG per subnet, and multiple subnets can share the same energy. Application security group or ASG handles functions such as web servers, application servers, or backend database servers combined to perform a meaningful service. Resource encryption brings the best of security with encryption in transit. SAP recommends using encryption at rest, so for the Azure storage account, we can use storage service encryption, which would use either Microsoft or customer-managed keys to manage encryption. Azure storage also adds encryption in transit, with SSL using HTTPS traffic. You can use Azure Disk Encryption (ADE) for OS and DBA encryption for SQL.

Migration of SAP Workloads to Azure

The most critical part of the migration is understanding what you are planning to migrate and accounting for dependencies, limitations, or even blockers that might stop your migration. Following an appropriate inventory process will ensure that your migration completes successfully. You can use in-hand tools to understand the current SAP landscape in the migration scope. For example, looking at your service now or CMDB catalog might reveal some of the data that expresses your SAP system. Then take that information to start drawing out your sizing in Azure. It is essential to ensure that we have a record of the current environment configuration, such as the number of servers and their names, server roles, and data about CPU and memory. It is essential to pick up the disk sizes, configuration, and throughput to ensure that your design delivers a better experience in Azure. It is also necessary to understand database replication and throughput requirements around replicas. When performing a migration, the sizing for large HANA instances is no different from sizing for HANA in general. For existing and deployment systems you want to move from other RDBMS to HANA, SAP provides several reports that run on your existing SAP systems. If migrating the database to HANA, these reports need to check the data and calculate memory requirements for the HANA instances.

When evaluating high availability and disaster recovery requirements, it is essential to consider the implications of choosing between two-tier and three-tier architectures. To avoid network contention in a two-tier arrangement, install database and Netweaver components on the same Azure VM. The database and application components get installed in three-tier configurations on separate Azure Virtual Machines. This choice has other implications regarding sizing since two-tier, and three-tier SAP ratings for a given VM differs. The high availability option is not mandatory for the SAP application servers.

You can achieve high availability by employing redundancy. To implement it, you can install individual application servers on separate Azure VMs. For example, you can achieve high availability for ASCS and SCS servers running on windows using windows failover clustering with SIOS data keeper. We can also achieve high availability with Linux clustering using Azure NetApp files. For DBMS servers, you should use DB replication technology using redundant nodes. Azure offers high availability through redundancy of its infrastructure and capabilities, such as Azure VM restarts, which play an essential role in a single VM deployment. In addition, Azure offers different SLAs depending on your configuration. For example, SAP landscapes organize SABC servers into different tiers; there are three diverse landscapes: deployment, quality assurance, and production.

Migration Strategies:- SAP landscapes to Azure

 Enterprises have SAP systems for business functions like Enterprise Resource Planning(ERP), global trade, business intelligence(BI), and others. Within those systems, there are different environments like sandbox developments, tests, and production. Each horizontal row is an environment, and each vertical dimension is the SAP system for a business function. The layers at the bottom are lower-risk environments and are less critical. Those towards the top are in high-risk environments and are more critical. As you move up the stack, there is more risk in the migration process. Production is the more critical environment. The use of test environments for business continuity is of concern. The systems at the bottom are smaller and have fewer computing resources, lower availability, size requirements, and less throughput. They have the same amount of storage as the production database with a horizontal migration strategy. To gain experience with production systems on Azure, you can use a vertical approach with low-risk factors in parallel to the horizontal design.

 Horizontal Migration Strategy

 To limit risk, start with low-impact sandboxes or training systems. Then, if something goes wrong, there is little danger associated with users or mission-critical business functions. After gaining experience in hosting, running, and administering SAP systems in Azure, apply to the next layer of systems up the stack. Then, estimate costs, limiting expenditures, performance, and optimization potential for each layer and adjust if needed.

Vertical Migration Strategy

The cost must be on guard along with legal requirements. Move systems from the sandbox to production with the lowest risk. First, the governance, risk, compliance system, and the object Event Repository gets driven towards production. Then the higher risk elements like BI and DRP. When you have a new system, it's better to start in Azure default mode rather than putting it on-premises and moving it later. The last system you move is the highest risk, mission-critical system, usually the ERP production system. Having the most performance virtual machines, SQL, and extensive storage would be best. Consider the earliest migration of standalone systems. If you have different SAP systems, always look for upstream and downstream dependencies from one SAP system to another.

Journey to SAP on Azure

Consider two main factors for the migration of SAP HANA to the cloud. The first is the end-of-life first-generation HANA appliance, causing customers to reevaluate their platform. The second is the desire to take advantage of the early value proposition of SAP business warehouse BW on HANA in a flexible DDA model over traditional databases and later BW for HANA. As a result, numerous initial migrations of SAP HANA to Microsoft Azure have focused on SAP BW to take advantage of SAP HANA's in-memory capability for the BW workloads. In addition, using the SAP database migration option DMO with the System Migration option of SUM facilitates single-step migration from the source system on-premises to the target system residing in Azure. As a result, it minimizes the overall downtime. In general, when initiating a project to deploy SAP workloads to Azure, you should divide it into the following phases. Project preparation and planning, pilot, non-production, production preparation, go-live, and post-production.

Use Cases for SAP Implementation in Microsoft Azure

 Use  cases

How  does Microsoft Azure help?

How  do organizations benefit?

Deliver  automated disaster recovery with low RPO and RTO

Azure  recovery services replicate on-premises virtual machines to Azure and  orchestrate failover and failback

RPO  and RTO get reduced, and the cost of ownership of disaster recovery (DR)  infrastructure diminishes. While the DR systems replicate, the only cost  incurred is storage

Make  timely changes to SAP workloads by development teams

200-300  times faster infrastructure provisioning and rollout compared to on-premises,  more rapid changes by SAP application teams

Increased  agility and the ability to provision instances within 20 minutes

Fund  intermittently used development and test infrastructure for SAP workloads

Supports  the potential to stop development and test systems at the end of business day

Savings  as much as 40-75 percent in hosting costs by exercising the ability to control  instances when not in use

Increase  data center capacity to serve updated SAP project requests

Frees  on-premises data center capacity by moving development and test for SAP  workloads to Microsoft Azure without upfront investments

Flexibility  to shift from capital to operational expenditures

Provide  consistent training environments based on templates

Ability  to store and use pre-defined images of the training environment for updated  virtual machines

Cost  savings by provisioning only the instances needed for training and then  deleting them when the event is complete

Archive  historical systems for auditing and governance

Supports  migration of physical machines to virtual machines that get activated when  needed

Savings  of as much as 60 percent due to cheaper storage and the ability to quickly  spin up systems based on need.

  References

n.d. Microsoft Azure: Cloud Computing Services. Accessed June 13, 2022. http://azure.microsoft.com.

n.d. All Blog Posts. Accessed June 13, 2022. https://blogs.sap.com.

n.d. Cloud4C: Managed Cloud Services for Enterprises. Accessed June 13, 2022. https://www.cloud4c.com.

n.d. NetApp Cloud Solutions | Optimized Storage In Any Cloud. Accessed June 13, 2022. http://cloud.netapp.com.

[andrewholland]

Top Benefits of Cloud Strategy Consulting for Healthcare Enterprises

[retrocgads]

USA 1990

[jcmarchi]

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series

New Post has been published on https://thedigitalinsider.com/dr-mike-flaxman-vp-or-product-management-at-heavy-ai-interview-series/

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series

Dr. Mike Flaxman is currently the VP of Product at HEAVY.AI, having previously served as Product Manager and led the Spatial Data Science practice in Professional Services. He has spent the last 20 years working in spatial environmental planning. Prior to HEAVY.AI, he founded Geodesign Technolgoies, Inc and cofounded GeoAdaptive LLC, two startups applying spatial analysis technologies to planning. Before startup life, he was a professor of planning at MIT and Industry Manager at ESRI.

HEAVY.AI is a hardware-accelerated platform for real-time, high-impact data analytics. It leverages both GPU and CPU processing to query massive datasets quickly, with support for SQL and geospatial data. The platform includes visual analytics tools for interactive dashboards, cross-filtering, and scalable data visualizations, enabling efficient big data analysis across various industries.

Can you tell us about your professional background and what led you to join HEAVY.AI?

Before joining HEAVY.AI, I spent years in academia, ultimately teaching spatial analytics at MIT. I also ran a small consulting firm, with a variety of public sector clients. I’ve been involved in GIS projects across 17 countries. My work has taken me from advising organizations like the Inter American Development Bank to managing GIS technology for architecture, engineering and construction at ESRI, the world’s largest GIS developer

I remember vividly my first encounter with what is now HEAVY.AI, which was when as a consultant I was responsible for scenario planning for the Florida Beaches Habitat Conservation Program.  My colleagues and I were struggling to model sea turtle habitat using 30m Landsat data and a friend pointed me to some brand new and very relevant data – 5cm LiDAR.   It was exactly what we needed scientifically, but something like 3600 times larger than what we’d planned to use.  Needless to say, no one was going to increase my budget by even a fraction of that amount. So that day I put down the tools I’d been using and teaching for several decades and went looking for something new.  HEAVY.AI sliced through and rendered that data so smoothly and effortlessly that I was instantly hooked.

Fast forward a few years, and I still think what HEAVY.AI does is pretty unique and its early bet on GPU-analytics was exactly where the industry still needs to go. HEAVY.AI is firmly focussed on democratizing access to big data. This has the data volume and processing speed component of course, essentially giving everyone their own supercomputer.  But an increasingly important aspect with the advent of large language models is in making spatial modeling accessible to many more people.  These days, rather than spending years learning a complex interface with thousands of tools, you can just start a conversation with HEAVY.AI in the human language of your choice.  The program not only generates the commands required, but also presents relevant visualizations.

Behind the scenes, delivering ease of use is of course very difficult.  Currently, as the VP of Product Management at HEAVY.AI, I’m heavily involved in determining which features and capabilities we prioritize for our products. My extensive background in GIS allows me to really understand the needs of our customers and guide our development roadmap accordingly.

How has your previous experience in spatial environmental planning and startups influenced your work at HEAVY.AI?

 Environmental planning is a particularly challenging domain in that you need to account for both different sets of human needs and the natural world. The general solution I learned early was to pair a method known as participatory planning, with the technologies of remote sensing and GIS.  Before settling on a plan of action, we’d make multiple scenarios and simulate their positive and negative impacts in the computer using visualizations. Using participatory processes let us combine various forms of expertise and solve very complex problems.

While we don’t typically do environmental planning at HEAVY.AI, this pattern still works very well in business settings.  So we help customers construct digital twins of key parts of their business, and we let them create and evaluate business scenarios quickly.

I suppose my teaching experience has given me deep empathy for software users, particularly of complex software systems.  Where one student stumbles in one spot is random, but where dozens or hundreds of people make similar errors, you know you’ve got a design issue. Perhaps my favorite part of software design is taking these learnings and applying them in designing new generations of systems.

Can you explain how HeavyIQ leverages natural language processing to facilitate data exploration and visualization?

These days it seems everyone and their brother is touting a new genAI model, most of them forgettable clones of each other.  We’ve taken a very different path.  We believe that accuracy, reproducibility and privacy are essential characteristics for any business analytics tools, including those generated with large language models (LLMs). So we have built those into our offering at a fundamental level.  For example, we constrain model inputs strictly to enterprise databases and to provide documents inside an enterprise security perimeter.  We also constrain outputs to the latest HeavySQL and Charts.  That means that whatever question you ask, we will try to answer with your data, and we will show you exactly how we derived that answer.

With those guarantees in place, it matters less to our customers exactly how we process the queries.  But behind the scenes, another important difference relative to consumer genAI is that we fine tune models extensively against the specific types of questions business users ask of business data, including spatial data.  So for example our model is excellent at performing spatial and time series joins, which aren’t in classical SQL benchmarks but our users use daily.

We package these core capabilities into a Notebook interface we call HeavyIQ. IQ is about making data exploration and visualization as intuitive as possible by using natural language processing (NLP). You ask a question in English—like, “What were the weather patterns in California last week?”—and HeavyIQ translates that into SQL queries that our GPU-accelerated database processes quickly. The results are presented not just as data but as visualizations—maps, charts, whatever’s most relevant. It’s about enabling fast, interactive querying, especially when dealing with large or fast-moving datasets. What’s key here is that it’s often not the first question you ask, but perhaps the third, that really gets to the core insight, and HeavyIQ is designed to facilitate that deeper exploration.

What are the primary benefits of using HeavyIQ over traditional BI tools for telcos, utilities, and government agencies?

HeavyIQ excels in environments where you’re dealing with large-scale, high-velocity data—exactly the kind of data telcos, utilities, and government agencies handle. Traditional business intelligence tools often struggle with the volume and speed of this data. For instance, in telecommunications, you might have billions of call records, but it’s the tiny fraction of dropped calls that you need to focus on. HeavyIQ allows you to sift through that data 10 to 100 times faster thanks to our GPU infrastructure. This speed, combined with the ability to interactively query and visualize data, makes it invaluable for risk analytics in utilities or real-time scenario planning for government agencies.

The other advantage already alluded to above, is that spatial and temporal SQL queries are extremely powerful analytically – but can be slow or difficult to write by hand.   When a system operates at what we call “the speed of curiosity” users can ask both more questions and more nuanced questions.  So for example a telco engineer might notice a temporal spike in equipment failures from a monitoring system, have the intuition that something is going wrong at a particular facility, and check this with a spatial query returning a map.

What measures are in place to prevent metadata leakage when using HeavyIQ?

As described above, we’ve built HeavyIQ with privacy and security at its core.  This includes not only data but also several kinds of metadata. We use column and table-level metadata extensively in determining which tables and columns contain the information needed to answer a query.  We also use internal company documents where provided to assist in what is known as retrieval-augmented generation (RAG). Lastly, the language models themselves generate further metadata.  All of these, but especially the latter two can be of high business sensitivity.

Unlike third-party models where your data is typically sent off to external servers, HeavyIQ runs locally on the same GPU infrastructure as the rest of our platform. This ensures that your data and metadata remain under your control, with no risk of leakage. For organizations that require the highest levels of security, HeavyIQ can even be deployed in a completely air-gapped environment, ensuring that sensitive information never leaves specific equipment.

How does HEAVY.AI achieve high performance and scalability with massive datasets using GPU infrastructure?

The secret sauce is essentially in avoiding the data movement prevalent in other systems.  At its core, this starts with a purpose-built database that’s designed from the ground up to run on NVIDIA GPUs. We’ve been working on this for over 10 years now, and we truly believe we have the best-in-class solution when it comes to GPU-accelerated analytics.

Even the best CPU-based systems run out of steam well before a middling GPU.  The strategy once this happens on CPU requires distributing data across multiple cores and then multiple systems (so-called ‘horizontal scaling’).  This works well in some contexts where things are less time-critical, but generally starts getting bottlenecked on network performance.

In addition to avoiding all of this data movement on queries, we also avoid it on many other common tasks.  The first is that we can render graphics without moving the data.  Then if you want ML inference modeling, we again do that without data movement.  And if you interrogate the data with a large language model, we yet again do this without data movement. Even if you are a data scientist and want to interrogate the data from Python, we again provide methods to do this on GPU without data movement.

What that means in practice is that we can perform not only queries but also rendering 10 to 100 times faster than traditional CPU-based databases and map servers. When you’re dealing with the massive, high-velocity datasets that our customers work with – things like weather models, telecom call records, or satellite imagery – that kind of performance boost is absolutely essential.

How does HEAVY.AI maintain its competitive edge in the fast-evolving landscape of big data analytics and AI?

That’s a great question, and it’s something we think about constantly. The landscape of big data analytics and AI is evolving at an incredibly rapid pace, with new breakthroughs and innovations happening all the time. It certainly doesn’t hurt that we have a 10 year headstart on GPU database technology. .

I think the key for us is to stay laser-focused on our core mission – democratizing access to big, geospatial data. That means continually pushing the boundaries of what’s possible with GPU-accelerated analytics, and ensuring our products deliver unparalleled performance and capabilities in this domain. A big part of that is our ongoing investment in developing custom, fine-tuned language models that truly understand the nuances of spatial SQL and geospatial analysis.

We’ve built up an extensive library of training data, going well beyond generic benchmarks, to ensure our conversational analytics tools can engage with users in a natural, intuitive way. But we also know that technology alone isn’t enough. We have to stay deeply connected to our customers and their evolving needs. At the end of the day, our competitive edge comes down to our relentless focus on delivering transformative value to our users. We’re not just keeping pace with the market – we’re pushing the boundaries of what’s possible with big data and AI. And we’ll continue to do so, no matter how quickly the landscape evolves.

How does HEAVY.AI support emergency response efforts through HeavyEco?

We built HeavyEco when we saw some of our largest utility customers having significant challenges simply ingesting today’s weather model outputs, as well as visualizing them for joint comparisons.  It was taking one customer up to four hours just to load data, and when you are up against fast-moving extreme weather conditions like fires…that’s just not good enough.

HeavyEco is designed to provide real-time insights in high-consequence situations, like during a wildfire or flood. In such scenarios, you need to make decisions quickly and based on the best possible data. So HeavyEco serves firstly as a professionally-managed data pipeline for authoritative models such as those from NOAA and USGS.  On top of those, HeavyEco allows you to run scenarios, model building-level impacts, and visualize data in real time.   This gives first responders the critical information they need when it matters most. It’s about turning complex, large-scale datasets into actionable intelligence that can guide immediate decision-making.

Ultimately, our goal is to give our users the ability to explore their data at the speed of thought. Whether they’re running complex spatial models, comparing weather forecasts, or trying to identify patterns in geospatial time series, we want them to be able to do it seamlessly, without any technical barriers getting in their way.

What distinguishes HEAVY.AI’s proprietary LLM from other third-party LLMs in terms of accuracy and performance?

Our proprietary LLM is specifically tuned for the types of analytics we focus on—like text-to-SQL and text-to-visualization. We initially tried traditional third-party models, but found they didn’t meet the high accuracy requirements of our users, who are often making critical decisions. So, we fine-tuned a range of open-source models and tested them against industry benchmarks.

Our LLM is much more accurate for the advanced SQL concepts our users need, particularly in geospatial and temporal data. Additionally, because it runs on our GPU infrastructure, it’s also more secure.

In addition to the built-in model capabilities, we also provide a full interactive user interface for administrators and users to add domain or business-relevant metadata.  For example, if the base model doesn’t perform as expected, you can import or tweak column-level metadata, or add guidance information and immediately get feedback.

How does HEAVY.AI envision the role of geospatial and temporal data analytics in shaping the future of various industries?

 We believe geospatial and temporal data analytics are going to be critical for the future of many industries. What we’re really focused on is helping our customers make better decisions, faster. Whether you’re in telecom, utilities, or government, or other – having the ability to analyze and visualize data in real-time can be a game-changer.

Our mission is to make this kind of powerful analytics accessible to everyone, not just the big players with massive resources. We want to ensure that our customers can take advantage of the data they have, to stay ahead and solve problems as they arise. As data continues to grow and become more complex, we see our role as making sure our tools evolve right alongside it, so our customers are always prepared for what’s next.

Thank you for the great interview, readers who wish to learn more should visit HEAVY.AI.