Azimut Group operates an international network of companies handling investment and asset management, mutual funds, hedge funds, and insurance. Founded in Milan, Italy in 1988, Azimut Group today has branches in fifteen countries, including Brazil, China, and the USA.

“We have subsidiaries and manage funds all over the world,” explains Simone Bertolotti, IT Manager at Azimut Holding S.p.a. “That means that any technology that we put in place has to cover needs from many different countries.”

“When complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”

—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.

Azimut manages its funds with investment advisors who use information sourced from Bloomberg, Reuters and others. “They use a huge amount of data,” says Simone. “They work with spreadsheets, algorithms, formulae and they analyse data in minutes.” In finance, every second is crucial, which is why Azimut decided to develop a risk management dashboard that can process information even more quickly, then distribute it worldwide.

“When an advisor manages data, that data is used to make immediate decisions on funds, capital movements or whether to sell stock,” says Simone. “They have to be ready to make recommendations for any amount of data that comes to them. For our dashboard, that means that when additional information arrives or complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”

Generating insights at speed

Investors and investment managers make decisions based on the most accurate, up-to-date information possible. For Azimut Group, information sourced through financial data vendors such as Bloomberg and Reuters provided only part of the data that the group required.

“We looked to collect information from a range of different providers,” explains Simone, “then analyse it to develop a predictive algorithm that could work faster than an advisor stationed at the terminal. We set ourselves the challenge to try to manipulate that data to add new insights into our matrix, so that every one of our branches across the world can see risk information about the funds in real-time.”

“We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling. With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it.

—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.

The first cloud provider Azimut used to build its system struggled to scale quickly to meet different kinds of data challenges. “If we wanted to add more cores, that was fine,” says Simone. “But the previous cloud provider made it complicated to raise the amount of space in a database infrastructure and scale up to demand. Scaling up for more in-depth analysis would take a day, and our need was immediate.”

That’s why Azimut switched one year ago to Google Cloud Platform to run the 150 VMs on its risk analysis platform. “We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling,” says Simone. “With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it. Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”

The infrastructure of Azimut’s solution handles around 800TB of data per month, and Google’s global network of servers and high-speed connections ensure that it gets to where it’s most needed by the most direct route. Impressed by the speed, security and availability of Google Cloud Platform, Azimut has moved its intranet on to Google Cloud Platform, too, eliminating the need for staff to login with VPNs.

“Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”

—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.

Driving ahead with Noovle

For Azimut, migrating the risk management dashboard is the latest of many Google product collaborations with cloud consultancy Noovle. “Everything started five years ago,” says Simone, “when Noovle assisted us in migrating to Gmail from our on-premise email solution. From G Suite to Google Cloud Platform, we’ve had a great relationship. Noovle provides consultancy services, support for mobility, and external advisors who work on our premises, such as when they trained us how to broadcast our meetings on Google Hangouts. As an independent company, we know we can trust them for transparent advice. All they care about is the best way to get a job done and to help us reach our goals.”

New app, new customers

In a business case comparison, Google Cloud Platform cost Azimut 35% less to run than the previous cloud provider. Now the group is building a major new mobile application on Google App Engine to be released in 2018.

“The new mobile application will allow customers to trade directly, without human advisors, by proposing different investment solutions depending on targets the customers set,” says Simone. “So if a customer aims to make money with investments, they enter their relevant personal information and we carry out the necessary regulatory checks and suggest what they could buy. The entire project will be based on Google Cloud Platform, so customers can control their investments through the app while we manage the fund, using Google Cloud Spanner on the backend.”

The popularity of meal kit delivery services has surged in recent years as consumer attitudes toward home cooking and grocery shopping have shifted. As a pioneer in the category, Blue Apron helps its customers create incredible home cooking experiences by sending culinary-driven recipes with high-quality ingredients and step-by-step instructions straight to customers’ doors. Blue Apron also offers a monthly wine subscription service and a la carte culinary tools and products through its marketplace.

If that sounds simple, it isn’t.

Ingredients for the meal kits must be sourced at the right time, quality, and price. Orders must be packed efficiently and in exactly the right proportions. Most importantly, meal kits must be delivered to the customer fresh and on time.

To meet these criteria and make data meaningful and intuitive to its managers, one of the tools Blue Apron relies on is Looker, an analytics platform that lets business users explore data and ask sophisticated questions using familiar terms. Looker integrates its solution with Google Cloud Platform to help customers modernize their analytics.

“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster. Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”

—Sam Chase, Tech Lead, Data Operations, Blue Apron

Blue Apron previously used Looker with a single database instance hosted on another cloud provider. As data volumes grew and queries became more complex, it became difficult to scale. Blue Apron’s only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. To improve speed, scalability, and cost efficiency, Blue Apron moved its data warehouse to Google BigQuery.

“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster,” says Sam Chase, Tech Lead, Data Operations at Blue Apron. “Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”

“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day.”

—Sam Chase, Tech Lead, Data Operations, Blue Apron

The analytics platform of the future

When you’re making business decisions about a customer’s dinner, speed matters. Looker takes full advantage of the power of Google BigQuery, making it easy to build a data exploration platform.

Blue Apron’s applications publish event data to Kafka—approximately 140 million events per day—and data is then streamed into Google BigQuery, which performs lightning-fast queries on both streamed and static data. Now, business users and analytics teams can make decisions based on near real-time information in Looker, instead of waiting until the next business day for results.

“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day,” says Sam.

Previously, Blue Apron spent up to a week out of every month optimizing its data warehouse to attempt to improve query performance. With Google BigQuery, all maintenance is handled by Google, reclaiming 25% of up to two engineers’ time. Even when multiple people are using Looker concurrently, query performance never degrades and storage never runs out.

“Because Google BigQuery is architected as a giant, shared cluster, growth is smooth,” says Lloyd Tabb, Founder and CTO of Looker. “Like a race car going from 0 to 120 mph, there are no shift points, just smooth acceleration. To us, it looks like the future.”

An empowering, integrated toolset

Looker takes advantage of aggressive caching and support for date-based table partitioning in Google BigQuery to increase performance, simplify the load process, and improve data manageability. By partitioning data by time, Blue Apron can also take advantage of better long-term storage pricing without sacrificing query performance. When using Google BigQuery with Looker, analysts can easily see how much data is going to be scanned before each query is run.

Blue Apron is also using Looker for Google BigQuery Data Transfer Service to provide actionable analytics for all of the company’s Google marketing data from Google AdWords and DoubleClick by Google in one place to understand campaign performance across channels, saving its data operations team months of work. Using Looker Blocks, marketers can quickly make sense of the data with reports and dashboards, and set alerts when campaign performance hits certain thresholds.

“Everyone at Blue Apron is excited about using Google BigQuery with Looker. Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”

—Sam Chase, Tech Lead, Data Operations, Blue Apron

Looker Blocks for Google AdWords and DoubleClick by Google provide all the analysis you’d get straight from the Google console, plus additional value-add analysis that’s impossible to replicate without SQL. Complex metrics such as ROI on ad spend, flexible multi-touch attribution, and predictive lifetime value empower marketers with a better understanding of their customers and where to spend their next dollar.

In addition to these turnkey dashboards and pieces of analysis, marketers can customize views to meet their unique needs and workflows. These capabilities help the Blue Apron marketing team make decisions regarding the allocation of spend to maximize customer acquisition and retention.

“Everyone at Blue Apron is excited about using Google BigQuery with Looker,” says Sam. “Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”

For data cleansing and transformation, Blue Apron uses Google Cloud Dataproc to run fully managed Apache Spark clusters on Google Cloud Platform. It’s also leveraging Google BigQuery integration with G Suite to bring data into Google Sheets for further distribution and analysis.

“Transferring data between Google tools is fast because it all happens on the Google network,” says Sam. “We can pull data from Google BigQuery, run transformations with Spark, and then write it back to Google BigQuery. That’s very helpful in providing our business users and data analysts with the richest, most current data.”

A perfect match for better insights

As Blue Apron seeks to expand its reach and deepen its engagement with customers, it is making Google BigQuery and Looker available to more users, providing a high-quality interactive analytics experience. “Our ability to pull a lot of data in and compute fast results affects everyone in our company,” says Sam. “Using Google BigQuery and Looker to iterate quickly and build new models to make our operations more efficient will directly impact our customers.”

For Looker, Google BigQuery represents the next step in data warehouse evolution. “Google BigQuery is a perfect match for Looker, combining easy setup with near infinite scale-out and elasticity,” says Lloyd. “People can make smarter decisions faster that directly benefit their business and customers.”

There are multiple reasons a growing number of database administrators, enterprise architects, application developers and other technology practitioners are moving to Google Cloud’s various database services.

Some are being driven by missing features in offering from other providers such as AWS. In Gartner’s Magic Quadrant for Operational Database Management Systems, the research and advisory firm points out that, “AWS’s surveyed reference customers scored its overall product capabilities one standard deviation (STD) below the mean. Their responses identified missing features such as multiregion writes and autosharding.”

Others are moving to database services on Google Cloud Platform driven by a few benefits. According to Gartner, “Reference customers repeatedly commented on Google’s ease of use and implementation, reliability and integration (with other services and other systems). Reference customers scored Google a full STD above the mean for satisfaction with GCP’s pricing; it received the second-highest satisfaction score of any vendor in this Magic Quadrant.

If you are looking to leverage the power of Google Cloud database offerings—but were unsure of which database services comes closest to the service you are currently using, here’s a handy map to find your way.

Google Cloud allows you to move your PostgreSQL databases to Cloud SQL with Database Migration Service (DMS). DMS gives you the ability to replicate data continuously to the destination database, while the source is live in production, enabling you to migrate with minimum downtime.

However, terabyte-scale migrations can be complex. For instance, if your PostgreSQL database has Large Objects, then you will require some downtime to migrate them manually as that is a limitation of DMS. There are few more such limitations – check out known limitations of DMS. If not handled carefully, these steps can extend the downtime during cutover, lead to performance impact on the source instance, or even delay the project delivery date. All this may mean significant business impact.

Searce is a technology consulting company, specializing in modernizing application and database infrastructure by leveraging cloud, data and AI. We empower our clients to accelerate towards the future of their business. In our journey, we have helped dozens of clients migrate to Cloud SQL, and have found terabyte-scale migrations to be the toughest for the reasons mentioned earlier.

This blog centers around our work in supporting an enterprise client whose objective was to migrate dozens of terabyte scale, mission-critical PostgreSQL databases to Cloud SQL with minimum downtime. Their largest database was 20TB in size and all the databases had tables with large objects and some tables did not have primary keys. Note that DMS had a limitation of not supporting migration of tables without a primary key during the time of this project. In June 2022, DMS released an enhancement to support the migration of tables without a primary key.

In this blog, we share with you our learnings about how we simplified and optimized this migration, so that you can incorporate our best practices into your own migrations. We explore mechanisms to reduce the downtime required for operations not handled by DMS by ~98% with the use of automation scripts. We also explore database flags in PostgreSQL to optimize DMS performance and minimize the overall migration time by ~15%.

Optimize DMS performance with database flags

Once the customer made the decision to migrate PostgreSQL databases to Google Cloud SQL, we considered two key factors that would decide business impact – migration effort and migration time. To minimize effort for the migration of PostgreSQL databases, we leveraged Google Cloud’s DMS (Database Migration Service) as it is very easy to use and it does the heavy lifting by continuously replicating data from the source database to the destination Cloud SQL instance, while the source database is live in production.

How about migration time? For a terabyte-scale database, depending on the database structure, migration time can be considerably longer. Historically, we observed that DMS took around 3 hours to migrate a 1 TB database. In other cases, where the customer database structure was more complex, migration took longer. Thankfully, DMS takes care of this replication while the source database is live in production, so no downtime is required during this time. Nevertheless, our client would have to bear the cost of both the source and destination databases which for large databases, might be substantial. Meanwhile, if the database size increased, then replication could take even longer, increasing the risk of missing the customer’s maintenance window for the downtime incurred during cutover operations. Since the customer’s maintenance window was monthly, we would have to wait for 30 more days for the next maintenance window, requiring the customer to bear the cost of both the databases for another 30 days. Furthermore, from a risk management standpoint, the longer the migration timeframe, the greater the risk that something could go wrong. Hence, we started exploring options to reduce the migration time. Even the slightest reduction in migration time could significantly reduce the cost and risk.

We explored options around tuning PostgreSQL’s database flags on the source database. While DMS has its own set of prerequisite flags for the source instance and database, we also found that flags like shared_buffers, wal_buffers and maintenance_work_mem helped accelerate the replication process through DMS. These flags needed to be set to a specific value to get the maximum benefit out of each of them. Once set, their cumulative impact was a reduction in time for DMS to replicate a 1 TB database by 4 hours, that is, reduction of 3.5 days for a 20 TB database. Let’s dive into each of them.

Shared Buffers

PostgreSQL uses two buffers – its own internal buffer and the kernel buffered IO. In other words, that data is stored in memory twice. The internal buffer is called shared_buffers, and it determines the amount of memory used by the database for the operating system cache. By default this value is set conservatively low. However, increasing this value on the source database to fit our use case helped increase the performance of read heavy operations, which is exactly what DMS does once a job has been initialized.

After multiple iterations, we found that if the value was set to 55% of the database instance RAM, it boosted the replication performance (a read heavy operation) by a considerable amount and in turn reduced the time required to replicate the data.

WAL Buffers

PostgreSQL relies on Write-Ahead Logging (WAL) to ensure data integrity. WAL records are written to buffers and then flushed to disk. The flag wal_buffers, determines the amount of shared memory used for WAL data that has not yet been written to disk – records that are yet to be flushed. We found that increasing the value for wal_buffers from the default value of 16MB to about 3% of the database instance’s RAM significantly improved the write performance by writing fewer but larger files to the disk at each transaction commit.

Maintenance Work Mem

PostgreSQL maintenance operations, such as VACUUM, CREATE INDEX, and ALTER TABLE ADD FOREIGN KEY, consume their own specific memory. This memory is referred to as maintenance_work_mem. Unlike other operations, PostgreSQL maintenance operations can only be performed sequentially by the database. Setting a value significantly higher than the default value of 64 MB meant that no maintenance operation would block the DMS job. We found that maintenance_work_mem worked best at the value of 1 GB.

Resize source instance to avoid performance impact

Each of these three flags tune how PostgreSQL utilizes memory resources. Hence, it was imperative that before setting these flags, we needed to upsize the source database instance to accommodate them. Without upsizing the database instances, we could have caused application performance degradation, as more than half of the total database memory would be allocated to the processes managed by these flags.

We calculated the memory required by the flags mentioned above, and found that each flag needed to be set to a specific percentage of the source instance’s memory, irrespective of the existing values that might be set for the flags:

• shared_buffers: 55% of source instance’s memory
• wal_buffers: 3% of source instance’s memory
• maintenance_work_mem: 1 GB

We added the individual memory requirements by the flags, and found that 58% of the RAM at least will be taken up by these memory flags. For example, if a source instance used 100GB of memory, 58GB would be taken up by shared_buffers and wal_buffers, and an additional 1GB by maintenance_work_mem. As the original value of these flags was very low (~200MB), we upsized the RAM of the source database instance by 60% in order to ensure that the migration did not impact source performance on the application live in production.

Avoid connection error with WAL sender timeout flag

While using Google Cloud’s DMS, if the connection is terminated between DMS and the Cloud SQL instance during the ‘Full Dump in Progress’ phase of the DMS job, the DMS job fails and needs to be reinitiated. Encountering timeouts, especially while migrating a terabyte-scale database, would mean multiple days’ worth of migration being lost and a delay in the cutover plan. For example, if the connection of the DMS job for a 20TB database migration is lost after 10 days, the DMS job will have to be restarted from the beginning, leading to 10 days’ worth of migration effort being lost.

Adjusting the WAL sender timeout flag (wal_sender_timeout) helped us avoid terminating replication connections that were inactive for a long time during the full dump phase. The default value for this flag is 60 seconds. To avoid these connections from terminating, and to avoid such high impact failures, we set the value of this flag to 0 for the duration of database migration. This would avoid connections getting terminated and allowed for smoother replication through the DMS jobs.

Generally, for all the database flags we talked about here, we advised our customer to restore the default flag values once the migration completed.

Reduce downtime required for DMS limitations by automation

While DMS does the majority of database migration through continuous replication when the source database instance is live in production, DMS has certain migration limitations that cannot be addressed when the database is live. For PostgreSQL, the known limitations of DMS include:

1. Any new tables created on the source PostgreSQL database after the DMS job has been initialized are not replicated to the destination PostgreSQL database.
2. Tables without primary keys on the source PostgreSQL database are not migrated. For those tables, DMS migrated only the schema. This is no longer a limitation after the June 2022 product update.
3. The large object (LOB) data type is not supported by DMS.
4. Only the schema for Materialized Views is migrated; the data is not migrated.
5. All data migrated is created under the ownership of cloudsqlexternalsync.

We had to address these aspects of the database migration manually. Since our client’s database had data with the large object data type, tables without primary keys, and frequently changing table structures that cannot be migrated by DMS, we had to manually export and import that data after DMS did most of the rest of the data migration. This part of database migration required downtime to avoid data loss. For a terabyte-scale database, this data can be in the hundreds of GBs, which means higher migration time and hence higher downtime. Furthermore, when you have dozens of databases to migrate, it can be stressful and error-prone for a human to perform these operations while on the clock during the cutover window!

This is where automation helped save the day! Automating the migration operations during the downtime period not only reduced the manual effort and error risk, but also provided a scalable solution that could be leveraged for the migration of 100s of PostgreSQL database instances to Cloud SQL. Furthermore, by leveraging multiprocessing and multithreading, we were able to reduce the total migration downtime for 100s of GBs of data by 98%, thereby reducing the business impact for our client.

How do we get there?

We laid out all the steps that need to be executed during the downtime – that is, after the DMS job has completed its replication from source to destination and before cutting over the application to the migrated database. You can see a chart mapping out the sequence of operations that are performed during the downtime period in Fig 1.

By automating all the downtime operations in this sequential approach, we observed that it took 13 hours for the entire downtime flow to execute for a 1 TB database. This included the migration of 250 MB in new tables, 60 GB in tables without primary keys and 150 GB in large objects.

One key observation we made was that, out of all the steps, only three steps took most of the time: migrating new tables, migrating tables without primary keys, and migrating large objects. These took the longest time because they all required dump and restore operations for their respective tables. However, these three steps did not have a hard dependency on each other as they individually targeted different tables. So we tried to run them in parallel as you can see in Fig 2. But the steps following them – ‘Refresh Materialized View’ and ‘Recover Ownership’ – had to be performed sequentially as they targeted the entire database.

However, running these three steps in parallel required upsizing the Cloud SQL instances, as we wanted to have sufficient resources available for each step. This led us to increase the Cloud SQL instances’ vCPU by 50% and memory by 40%, since the export and import operations depended heavily on vCPU consumption as opposed to memory consumption.

Migrating the new tables (created after the DMS job was initiated) and tables without primary keys was straightforward as we were able to leverage the native utilities offered by PostgreSQL – pg_dump and pg_restore. Both utilities process tables in parallel by using multiple threads– the higher the table count, the higher the number of threads that could be executed in parallel, allowing faster migration. With this revised approach, for the same 1 TB database, it still took 12.5 hours for the entire downtime flow to execute.

This improvement reduced the cutover downtime, but we still found that we needed a 12.5 hour window to complete all the steps. We then discovered that 99% of the time of downtime was taken up by just one step: exporting and importing 150 GB of large objects. It turned out that multiple threads could not be used to accelerate the dump and restore large objects in PostgreSQL. Hence, migrating the large objects single handedly extended the downtime for migration by hours. Fortunately, we were able to come up with a workaround for that.

Optimize migration of Large Object from PostgreSQL database

PostgreSQL contains a large objects facility that provides stream-style access to data stored in a special large-object structure. When large objects are stored, they are broken down into multiple chunks and stored in different rows of the database, but are connected under a single Object Identifier (OID). This OID can thus be used to access any stored Large Object. Although users can add large objects to any table in the database, under the hood, PostgreSQL physically stores all large objects within a database in a single table called pg_largeobjects.

While leveraging pg_dump and pg_restore for export and import of large objects, this single table – pg_largeobject, becomes a bottleneck as the PostgreSQL utilities cannot execute multiple threads for parallel processing, since it’s just one table. Typically, the order of operations for these utilities looks something like this:

1. pg_dump reads the data to be exported from the source database
2. pg_dump writes that data into the memory of the client where pg_dump is being executed
3. pg_dump writes from memory to the disk of the the client (a second write operation)
4. pg_restore reads the data from the client’s disk
5. pg_restore writes the data to the destination database

Normally, these utilities would need to be executed sequentially to avoid data loss or data corruption due to conflicting processes. This leads to further increase in migration time for large objects.

Our workaround for this single-threaded process involved two elements. First, with our solution, we eliminated the second write operation – write from memory to disk (point #3). Instead, once the data was read and written into memory, our program would begin the import process and write data to the destination database. Second, since pg_dump and pg_restore could not use multiple threads to process the large objects in just the pg_largeobjects table, we took it upon ourselves to develop a solution that could use multiple threads. The thread count was based on the number of OIDs in the table – pg_largeobjects, and break that single table into smaller chunks for parallel execution.

This approach brought down Large Object migration operation from hours to minutes, therefore bringing down the downtime needed for all operations to be completed that DMS cannot handle, for the same 1 TB database, from 13 hours to just 18 minutes. A reduction of ~98% in the required downtime.

Conclusion

After multiple optimizations and dry runs, we were able to develop a procedure for our client to migrate dozens of terabyte-scale PostgreSQL databases to Google Cloud SQL with a minimal business impact. We developed practices to optimize DMS-based migration by 15% using database flags and reduce downtime by 98% with the help of automation and innovation. These practices can be leveraged for any terabyte-scale migration of PostgreSQL databases to Google Cloud SQL to accelerate migration, minimize downtime and avoid performance impact on mission critical applications.

Oden Technologies is using the Internet of Things (IoT) to improve the factories of today. The giant network of “things” (including people) connected to each other via the Internet has the potential to reduce waste, increase efficiency, and improve safety across all walks of life. Oden is leading IoT innovation in manufacturing by combining wireless connectivity, big data, and cloud computing.

The use of data to improve manufacturing is practically as old as manufacturing itself. But the computerization of manufacturing has resulted in broad and rapid changes to the way data is collected and processed, as well as the sheer volume of data available.

Oden’s goal is to help manufacturers tap into this data to quickly identify process trends and even warning signs of machine breakdown. Such visibility can reveal opportunities to improve manufacturing and maintenance processes that reduce waste and increase profit margins.

Oden designs and develops data collection devices that can plug into almost any kind of machine and can wirelessly transmit data with minimal complexity and setup time.

Once devices are installed, the Oden technology platform processes data to give manufacturers cutting-edge analytics that are easy to comprehend. Analysis produced by the platform provides factory engineers with data points such as detailed root-cause analysis down to the second, factory-wide performance in real-time, and trend analysis.

Improving cloud delivery

Oden’s previous cloud platform performed satisfactorily, but the company evaluated alternatives in search of potential reductions in cost and complexity and increases in performance.

When evaluating Google Cloud Platform, Oden discovered it would require fewer virtual machine (VM) instances for equivalent performance, which would cut costs. Furthermore, Oden could gain more sophisticated data analytics and machine learning capabilities compared with its existing cloud provider.

Today, Oden runs its entire platform on Google Cloud Platform including Google Compute Engine, Google Cloud Pub/Sub, Google Cloud Bigtable, Google Stackdriver, and Google Kubernetes Engine.

“In order to serve our customers, we need a cloud platform that can scale reliably while keeping costs low, perform under heavy loads, and consistently deliver sophisticated features such as machine learning,” says Willem Sundblad, CEO and Founder at Oden Technologies. “Google Cloud Platform is way ahead in all of these areas compared to our previous cloud provider.”

Capturing tens of millions of metrics a day

Using Google Cloud Platform, Oden can help an average factory capture and store approximately 10 million metrics on a single manufacturing line every day.

Metrics can include extremely granular detail, such as the amount of electricity going to machines, the amount of raw material consumed, and the volume of material produced. Sensors can also capture and transmit environmental information such as temperature, humidity, and dew point so that manufacturers can identify weather-related and seasonal impacts on production.

The updated Oden Cloud Platform uses Kubernetes Engine—powered by the open source Kubernetes system—to run application program interfaces (APIs) that capture data from Oden’s wireless devices on the factory floor.

Google Cloud Pub/Sub then sends the data in real time to Google Cloud Bigtable, where data is processed using Oden’s proprietary analytics tools. Google Stackdriver supports Google Cloud Platform monitoring, logging, and diagnostics, which help Oden deliver its cloud platform with confidence.

Oden Technologies builds dashboards powered by Kubernetes Engine, which pull analyzed data from Google Cloud Bigtable. The dashboards provide customers with real-time visibility into their manufacturing lines. Oden Factory Cloud dashboards allow customers to delve deeper into their data to fine-tune production processes or discover the root causes of production issues.

With the previous cloud provider, Oden required 80 VM instances to run the dashboards. With Google Cloud Platform that number has been cut to 45, which dramatically reduces costs and complexity.

“We migrated from our previous cloud provider to Google Cloud Platform in just one month,” says Willem. “Further, our storage and data analytics costs have decreased by 30%. Cost savings like these allow us to protect customers from rising expenses, keeping us focused on bringing the best products possible to market.”

Faster data access; more efficient factories

With Google Cloud Platform, Oden can now deliver a complete factory analytics picture to manufacturers. In environments where thousands of variables affect the bottom line, businesses can now automatically and perpetually record machine and performance measurement. Oden Factory Cloud gives customers access to comprehensive data insights and can eliminate reliance on onsite infrastructure investments to run their own analytics.

Because manufacturers have access to live data and can analyze production data quickly, they can troubleshoot and resolve problems in minutes rather than months. Such information helps improve product quality, minimize unplanned downtime, cut costs, and improve profitability.

“With the help of Google Cloud Platform, we are helping our customers to be data-driven, which wasn’t possible before,” adds Willem. “They now understand that data is their most important asset. That allows them to be more innovative and continually improve their production processes.”