B2B marketplace IndiaMART aims to help businesses escape the restrictions of traditional supply chains. By providing access to a digital platform optimized for access from desktops and mobile devices, businesses can improve their operations and generate more revenue. IndiaMART’s suite of services includes web storefront, enquiry support, priority listings, premium number services, a lead management system, and payment facilitation.

IndiaMART also provides “behavior-based matchmaking” that identifies the supplier best equipped to meet buyer needs by product or service, category and location. IndiaMART then matches the designated suppliers with the buyers. Finally, IndiaMART operates as a “horizontal marketplace”—enabling suppliers to market to a large number of potential buyers—presenting a compelling offering for both groups.

Amarinder S. Dhaliwal, Chief Product Officer of IndiaMART, says once IndiaMART grew to a certain size, it benefited from a network effect—as more buyers used the marketplace, more suppliers came on board, prompting yet more buyers to access the service and so on. Suppliers becoming buyers and using IndiaMART to purchase products is another growth driver. “There is not just a network effect, but a community effect as well, as a supplier becomes a buyer,” says Dhaliwal. “This increases affinity, and supplier and buyer ‘lock-in’ to the marketplace increases multifold.”

Positioned to address challenges

IndiaMART’s proactive approach positioned the business well to address the challenges presented by new trends and market conditions. Traffic from mobile devices to its business-to-business marketplace has grown from about 30% to 75% over the last four years.

“Mobile traffic has grown at a compound annual growth rate of almost 100% over the same period,” says Dhaliwal. “The proliferation of smartphones and other mobile devices has brought a considerable number of new users onto the internet and these users look for value—the right price from the right supplier,” he adds. “Furthermore, they can connect at any time and from any location they can access a network.”

The mobility revolution also challenged IndiaMART to provide a user interface and experience optimized for devices of various types and sizes—and that incorporated screens much smaller than the screens incorporated in desktops. The organization also had to help users overcome issues such as inconsistent network coverage and quality—particularly in remote areas.

Becoming a mobile-first organization

IndiaMART is responding by becoming, for buyers, a “mobile-first” organization that meets the group’s technical and user experience requirements.

IndiaMART is also adapting its marketplace to support two key trends:
• Buyers using long, conversational sentences to conduct online searches rather than simply typing in keywords
• Non English-users—the vast majority of people in India—stepping up their use of the marketplace

“We expect that, within a few years, we will have more non-English users than English users on IndiaMART,” says Dhaliwal.

IndiaMART is also benefiting from Indian government measures to reform taxation and stimulate the digital economy. “There has been a huge focus on areas such as digital payments and the digitization of identity,” says Dhaliwal. “We have embraced elements of this agenda by implementing a digital payment platform and are continuing to look at ways of providing new digital services to suppliers.

“Meanwhile, the Indian government’s recent implementation of GST allows us to validate suppliers’ businesses and bring more qualified, more verified suppliers on our platform—improving the experience for buyers and suppliers.”

Speed and reliability an issue

IndiaMART had started operations with servers, storage, networking, and associated systems co-located in a data center in the United States. However, as buyers and suppliers increasingly used mobile devices—over occasionally unreliable networks—to access the marketplace, access speeds and reliability became an issue. With most requests traveling between India and the United States, network latency was unacceptably high. Furthermore, business growth meant IndiaMART needed an environment that could scale to meet demand for the next five to 10 years.

IndiaMART opted for a multi-cloud architecture and established criteria for cloud providers to win its business. “We required an infrastructure that could scale and meet our demand for fast response time without putting our business at risk,” says Dhaliwal. “This meant taking a phased rather than one-shot approach to the migration. We also needed to minimize any wasteful duplication of infrastructure and reduce latency. In addition, as we scaled, we needed to protect our systems, transactions and information, including the details of buyers and suppliers.”

Google Cloud team’s high-quality support

The organization performed proof of concept with the three largest multinational cloud services providers and found Google Cloud was best positioned to act as the cornerstone of its multi-cloud architecture. “The Google Cloud team gave us considerable support in helping us run a proof of concept of its services,” says Dhaliwal.

“The proof of concept also illustrated that Google Cloud was superior to the other cloud services we looked at.

“We could run our marketplace across multiple geo-locations under a single IP address, avoiding duplication, and users in India could connect to Google Cloud via the closest access point, with their traffic passing quickly across the Google network.

“In addition, Google Cloud’s load balancing service would enable encryption between load balancing layers and back ends to ensure security, while all communication would move across Google’s own protected network.”

Being one of India’s early users of G Suite, the organization was also familiar with Google Cloud applications and services.

IndiaMART then opted to work with the Google Cloud team and a certified partner on a step-by-step implementation that minimized any risk of disruption.

Deep engagement from Google

“We engaged very deeply with both Google and the partner to complete this migration,” says Dhaliwal. “The Google team worked very hard to understand our requirements and provide a solution that catered to our needs and could be deployed in a phased manner.” The team ran workshops and technical sessions with IndiaMART and, at a Google Summit, connected the marketplace provider to Google experts in databases and infrastructure.

“These discussions really helped us formulate a strategy moving forward,” says Dhaliwal.

Based on input from Google and its own evaluation, IndiaMART developed an architecture comprising virtual machine instances delivered through Compute Engine, Google Cloud’s infrastructure-as-a-service offering; Cloud Load Balancing to support cloud resources distributed across multiple locations; Cloud Pub/Sub to provide enterprise messaging; and Cloud Dataflow to transform and enrich data.

Cloud Armor works with Cloud Load Balancing to defend against distributed denial of service (DDoS) attacks; and Geocoding API helps the organization convert geographic coordinates into readable addresses and vice versa. Cloud AutoML allows IndiaMART to train machine learning models to meet its requirements. With Geocoding API, IndiaMART can matchmake buyers and suppliers based on location—providing a high quality experience for both parties. Finally, AutoML Translation allows the organization to create a custom machine learning model that converts product names from English into Hindi and other languages, effectively opening up new markets for buyers and suppliers.

Phase one complete

IndiaMART has completed phase one of the migration that involved moving its web properties across to Google Cloud. The organization is now experimenting with moving its APIs and databases to the service and anticipates completing the exercise over the coming year.

Average page load time down

The initial phase of the project has already delivered considerable benefits to IndiaMART. The organization has cut average page loading time from five seconds to three seconds, and Dhaliwal attributes close to one second of that reduction to the move to Google Cloud. “With Google Cloud, buyers and suppliers can access our marketplace much faster than previously,” says Dhaliwal. “This impacts positively on engagement, time spent on our marketplace, and the user’s entire journey with us.”

DDoS attack repelled

Google Cloud’s security features have already passed their first test. As IndiaMART undertook stage one of the migration, the business experienced a DDoS attack that generated request loads more than 400 times greater than normal. “Because we were on Google Cloud infrastructure, we could develop a solution to combat this severe DDoS attack,” says Sunil Parolia, Sr. VP at IndiaMART. “From a security perspective, this really justified our decision to go with Google Cloud.”

Google Cloud is also helping deliver the availability required by IndiaMART and the scalability to support growing demand. “As the number of people in India who access the internet grows from about 500 million to 700-800 million over the next couple of years, we will continue to build our traffic and be the dominant business-to-business platform,” says Dhaliwal. “On the supplier side, we expect to see more and more businesses come onto our marketplace—ranging from small-to-medium businesses up to larger brands. Google Cloud will enable us to accommodate this traffic without compromising the experience we provide.”

At Google Cloud, being a strategic partner is part of our DNA. Whether it’s listening closely to our customers, helping to build team skills for innovation or simply being there (since we know the cloud is 24/7), we get excited about working hands-on with customers to deliver new solutions. 

As we look to solve decades-old challenges with new technologies in workforce productivity, cybersecurity, and artificial intelligence/machine learning, we know that we are only as good as the people behind the technology. Today, we’re proud to spotlight a few of the inspiring folks behind Google Cloud Public Sector and celebrate their recognition in the industry. 

Melissa Adamson, Head of Government Channels at Google Cloud, has been named to the highly respected Women of the Channel list for 2021. This annual list recognizes the unique strengths, leadership and achievements of female leaders in the IT channel. The women honored this year pushed forward with comprehensive business plans, marketing initiatives and innovative ideas to support their partners and customers.

Melissa was brought on to build the Public Sector channel from scratch. The initial focus was building the channel for the US government team and has since expanded to include education, Canada and Latin America.

Having a career background at both Microsoft and Accenture, Melissa leveraged her extensive professional network to build the organic partnerships needed to accelerate the Public Sector partner ecosystem. This helped her drive two key wins (US Postal Service and PTO) and personally recruit top cloud partners in the industry. Melissa loves card games and is learning a new language.

Todd Schoeder, Director of Global Public Sector Digital Strategy, was recently featured in the “Top 20 Cloud Executives to Watch in 2021” by Wash Exec. Recognized for his work in helping customers navigate through the impact of COVID-19 and developing innovative solutions to meet mission challenges, he says: “New partnerships are required to solve for the problems of the future. Challenges that were previously thought of as insurmountable, too risky or expensive, are actually quite the opposite — as long as you have the right partner that is working in your best interest with you.”

Josh Marcuse, Head of Strategy & Innovation, received his second Wash100 Award for leading a digital transformation team that works to drive the development of public sector solutions, including cyber defense, smart cities, and public health.

Josh has launched services to support collaborative team operations including Workspace for Government and an artificial intelligence-based customer service platform to support remote work needs. His work also includes leading Google Cloud’s partnerships with organizations to improve data sharing in the public health community, contact tracing activities, and supporting research efforts across national laboratories. 

Like Melissa, Josh was brought on to build a new team dedicated to strategy and innovation. This team’s purpose is to bring an intense focus to public sector mission outcomes and the public servants who own them. Josh spent a decade pushing digital modernization and workforce transformation at the U.S. Department of Defense, and co-founded the Federal Innovation Council at the Partnership for Public Service, and now brings that domain expertise to supporting government workers who are driving digital transformation.

Join us in celebrating these folks for their leadership and contributions!

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

• Atomic: All operations in a transaction succeed or the operation is rolled back.
• Consistent: On the completion of a transaction, the database is structurally sound.
• Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
• Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

• Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
• Horizontal scaling, usually using hashed distributions
• Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
• Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

• Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
• Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
• In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
• Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
• Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

• Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
• Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
• Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.

As an SAP user, you may already be familiar with SAP Landscape Management (SAP LaMa) — a specialized product that supports centralized management of your SAP landscape. SAP LaMa is useful for a wide range of SAP customers and use cases, but it’s especially important to large enterprises that manage very large, diverse, and often geographically dispersed SAP landscapes.

Google Cloud recently released a free connector that our SAP customers can deploy on an existing or new SAP LaMa instance. It’s a potentially important contribution for enterprises that need to address the growing complexity associated with multi-cloud and hybrid-cloud application strategies.

Let’s take a look at how SAP LaMa works, how it fits into a customer’s SAP landscape, and what kinds of benefits they can expect. We’ll also run through some adjustments that SAP admins will need to make in order to launch the connector.

SAP LaMa: Bringing simplicity to SAP landscapes

The Google Cloud Connector for SAP LaMa is distributed as a Java archive — provided free of charge — that customers can deploy on a SAP LaMa system that already exists on premises, or on another cloud, or as part of a new SAP LaMa deployment on Google Cloud. SAP LaMa itself is a centralized SAP management tool designed to simplify, automate, and orchestrate a variety of management and administration tasks across your entire SAP landscape. Some examples of use cases for LaMa include:

• Getting a single, comprehensive overview of your organization’s full SAP landscape
• Automating day-to-day system administration tasks, like system refreshes
• Executing mass stop/start operations across your landscape
• Deploying custom operations and workflows within the SAP application environment

SAP admins can reclaim a significant amount of time by creating SAP context sensitive automation routines that can execute without human intervention, and by completing system maintenance tasks more quickly and consistently. SAP LaMa also elevates service quality by using automation to remove manual intervention (and thus human error) from the system admin process.

The Google Cloud Connector builds bridges for SAP customers

The Google Cloud connector functions as a trigger that extends LaMa functionality to SAP systems deployed on Google Cloud. The connector enables standard SAP LaMa execution scenarios, such as:

• Listing Google Cloud projects, zones, and VM instances within SAP LaMa’s landscape overview
• Mass stop/start of Google Compute Engine (GCE) instances, using either the SAP LaMa user interface or the scheduler
• System clone, copy, and DB refresh with Post Copy Automation
• Resizing of machine types
• Relocating SAP application instances to another VM instance
• SAP HANA failover to a replicated SAP HANA HA system  via the SAP Host Agent

By supporting native SAP automation and orchestration, the Google Cloud connector contributes to the overall value customers get from using SAP LaMa. What may be just as important, however, is how the connector makes life easier for customers running Google Cloud as part of a hybrid cloud or multi-cloud SAP landscape. That’s an increasingly important advantage, given recent research revealing that 82% of enterprises are currently deploying hybrid clouds.¹

Before getting started with the Google Cloud connector, it’s important to be aware that you’ll have to run it on SAP LaMa 3.0 (the current version), and that you will need to have an SAP Landscape Management – Enterprise Edition licence provided by SAP to support all functionality.

In addition, getting access to the full SAP LaMa feature set for your Google Cloud SAP environment will require you to deploy SAP systems using SAP’s adaptive design principles. These include:

• Using SID (SAP system ID) and instance-specific disk mount points
• Using alias IP for virtual host mapping and portability
• Configuring DNS to manage virtual hostname (aka. FQDN) for all VMs
• Using a local NFS host to manage shared files, as well as copying, migrating, and managing solutions like Filestore and NetApp separately

Because meeting these requirements may require altering your production systems, you’ll have to assess the pros and cons of doing so to take full advantage of the Google Cloud Connector.

Google Cloud is constantly looking for ways to support our SAP customers in running more efficiently and in making the most of their hybrid cloud and multi-cloud strategies. Releasing the Google Connector for SAP LaMa is one more reflection of our commitment to creating value for customers any way we can. Learn more about Google Cloud offerings for SAP customers.