Personal blog of Yzmir Ramirez

Berlin-based y42 (formerly known as Datos Intelligence), a data warehouse-centric business intelligence service that promises to give businesses access to an enterprise-level data stack that’s as simple to use as a spreadsheet, today announced that it has raised a $2.9 million seed funding round led by La Famiglia VC. Additional investors include the co-founders of Foodspring, Personio and Petlab.

The service, which was founded in 2020, integrates with more than 100 data sources, covering all the standard B2B SaaS tools, from Airtable to Shopify and Zendesk, as well as database services like Google’s BigQuery. Users can then transform and visualize this data, orchestrate their data pipelines and trigger automated workflows based on this data (think sending Slack notifications when revenue drops or emailing customers based on your own custom criteria).

Like similar startups, y42 extends the idea data warehouse, which was traditionally used for analytics, and helps businesses operationalize this data. At the core of the service is a lot of open source and the company, for example, contributes to GitLabs’ Meltano platform for building data pipelines.

y42 founder and CEO Hung Dang. Image Credits: y42

“We’re taking the best of breed open-source software. What we really want to accomplish is to create a tool that is so easy to understand and that enables everyone to work with their data effectively,” Y42 founder and CEO Hung Dang told me. “We’re extremely UX obsessed and I would describe us as a no-code/low-code BI tool — but with the power of an enterprise-level data stack and the simplicity of Google Sheets.”

Before y42, Vietnam-born Dang co-founded a major events company that operated in more than 10 countries and made millions in revenue (but with very thin margins), all while finishing up his studies with a focus on business analytics. And that in turn led him to also found a second company that focused on B2B data analytics.

Image Credits: y42

Even while building his events company, he noted, he was always very product- and data-driven. “I was implementing data pipelines to collect customer feedback and merge it with operational data — and it was really a big pain at that time,” he said. “I was using tools like Tableau and Alteryx, and it was really hard to glue them together — and they were quite expensive. So out of that frustration, I decided to develop an internal tool that was actually quite usable and in 2016, I decided to turn it into an actual company. ”

He then sold this company to a major publicly listed German company. An NDA prevents him from talking about the details of this transaction, but maybe you can draw some conclusions from the fact that he spent time at Eventim before founding y42.

Given his background, it’s maybe no surprise that y42’s focus is on making life easier for data engineers and, at the same time, putting the power of these platforms in the hands of business analysts. Dang noted that y42 typically provides some consulting work when it onboards new clients, but that’s mostly to give them a head start. Given the no-code/low-code nature of the product, most analysts are able to get started pretty quickly — and for more complex queries, customers can opt to drop down from the graphical interface to y42’s low-code level and write queries in the service’s SQL dialect.

The service itself runs on Google Cloud and the 25-people team manages about 50,000 jobs per day for its clients. The company’s customers include the likes of LifeMD, Petlab and Everdrop.

Until raising this round, Dang self-funded the company and had also raised some money from angel investors. But La Famiglia felt like the right fit for y42, especially due to its focus on connecting startups with more traditional enterprise companies.

“When we first saw the product demo, it struck us how on top of analytical excellence, a lot of product development has gone into the y42 platform,” said Judith Dada, general partner at LaFamiglia VC. “More and more work with data today means that data silos within organizations multiply, resulting in chaos or incorrect data. y42 is a powerful single source of truth for data experts and non-data experts alike. As former data scientists and analysts, we wish that we had y42 capabilities back then.”

Dang tells me he could have raised more but decided that he didn’t want to dilute the team’s stake too much at this point. “It’s a small round, but this round forces us to set up the right structure. For the Series A, which we plan to be towards the end of this year, we’re talking about a dimension which is 10x,” he told me.

Data is a gold mine for a company.

If managed well, it provides the clarity and insights that lead to better decision-making at scale, in addition to an important tool to hold everyone accountable.

However, most companies are stuck in Data 1.0, which means they are leveraging data as a manual and reactive service. Some have started moving to Data 2.0, which employs simple automation to improve team productivity. The complexity of crypto data has opened up new opportunities in data, namely to move to the new frontier of Data 3.0, where you can scale value creation through systematic intelligence and automation. This is our journey to Data 3.0.

Coinbase is neither a finance company nor a tech company — it’s a crypto company. This distinction has big implications for how we work with data. As a crypto company, we work with three major types of data (instead of the usual one or two types of data), each of which is complex and varied:

1. Blockchain: decentralized and publicly available.
2. Product: large and real-time.
3. Financial: high-precision and subject to many financial/legal/compliance regulations.

Image Credits: Michael Li/Coinbase

Our focus has been on how we can scale value creation by making this varied data work together, eliminating data silos, solving issues before they start and creating opportunities for Coinbase that wouldn’t exist otherwise.

Having worked at tech companies like LinkedIn and eBay, and also those in the finance sector, including Capital One, I’ve observed firsthand the evolution from Data 1.0 to Data 3.0. In Data 1.0, data is seen as a reactive function providing ad-hoc manual services or firefighting in urgent situations.

At its Ignite conference today, Microsoft announced the launch of Azure Managed Instance for Apache Cassandra, its latest NoSQL database offering and a competitor to Cassandra-centric companies like Datastax. Microsoft describes the new service as a ‘semi-managed offering that will help companies bring more of their Cassandra-based workloads into its cloud.

“Customers can easily take on-prem Cassandra workloads and add limitless cloud scale while maintaining full compatibility with the latest version of Apache Cassandra,” Microsoft explains in its press materials. “Their deployments gain improved performance and availability, while benefiting from Azure’s security and compliance capabilities.”

Like its counterpart, Azure SQL Manages Instance, the idea here is to give users access to a scalable, cloud-based database service. To use Cassandra in Azure before, businesses had to either move to Cosmos DB, its highly scalable database service which supports the Cassandra, MongoDB, SQL and Gremlin APIs, or manage their own fleet of virtual machines or on-premises infrastructure.

Cassandra was originally developed at Facebook and then open-sourced in 2008. A year later, it joined the Apache Foundation and today it’s used widely across the industry, with companies like Apple and Netflix betting on it for some of their core services, for example. AWS launched a managed Cassandra-compatible service at its re:Invent conference in 2019 (it’s called Amazon Keyspaces today), Microsoft launched the Cassandra API for Cosmos DB in September 2018. With today’s announcement, though, the company can now offer a full range of Cassandra-based servicer for enterprises that want to move these workloads to its cloud.

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Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.

The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.

The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).

Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.

Image Credits: Census

“Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless,” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.“

Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.

Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into an enterprise orchestration system.

Datastax, the company best known for commercializing the open-source Apache Cassandra database, is moving beyond databases. As the company announced today, it has acquired Kesque, a cloud messaging service.

The Kesque team built its service on top of the Apache Pulsar messaging and streaming project. Datastax has now taken that team’s knowledge in this area and, combined with its own expertise, is launching its own Pulsar-based streaming platform by the name of Datastax Luna Streaming, which is now generally available.

This move comes right as Datastax is also now, for the first time, announcing that it is cash-flow positive and profitable, as the company’s chief product officer, Ed Anuff, told me. “We are at over $150 million in [annual recurring revenue]. We are cash-flow positive and we are profitable,” he told me. This marks the first time the company is publically announcing this data. In addition, the company also today revealed that about 20 percent of its annual contract value is now for DataStax Astra, its managed multi-cloud Cassandra service and that the number of self-service Asta subscribers has more than doubled from Q3 to Q4.

The launch of Luna Streaming now gives the 10-year-old company a new area to expand into — and one that has some obvious adjacencies with its existing product portfolio.

“We looked at how a lot of developers are building on top of Cassandra,” Anuff, who joined Datastax after leaving Google Cloud last year, said. “What they’re doing is, they’re addressing what people call ‘data-in-motion’ use cases. They have huge amounts of data that are coming in, huge amounts of data that are going out — and they’re typically looking at doing something with streaming in conjunction with that. As we’ve gone in and asked, “What’s next for Datastax?,’ streaming is going to be a big part of that.”

Given Datastax’s open-source roots, it’s no surprise the team decided to build its service on another open-source project and acquire an open-source company to help it do so. Anuff noted that while there has been a lot of hype around streaming and Apache Kafka, a cloud-native solution like Pulsar seemed like the better solution for the company. Pulsar was originally developed at Yahoo! (which, full disclosure, belongs to the same Verizon Media Group family as TechCrunch) and even before acquiring Kesque, Datastax already used Pulsar to build its Astra platform. Other Pulsar users include Yahoo, Tencent, Nutanix and Splunk.

“What we saw was that when you go and look at doing streaming in a scale-out way, that Kafka isn’t the only approach. We looked at it, and we liked the Pulsar architecture, we like what’s going on, we like the community — and remember, we’re a company that grew up in the Apache open-source community — we said, ‘okay, we think that it’s got all the right underpinnings, let’s go and get involved in that,” Anuff said. And in the process of doing so, the team came across Kesque founder Chris Bartholomew and eventually decided to acquire his company.

The new Luna Streaming offering will be what Datastax calls a “subscription to success with Apache Pulsar.’ It will include a free, production-ready distribution of Pulsar and an optional, SLA-backed subscription tier with enterprise support.

Unsurprisingly, Datastax also plans to remain active in the Pulsar community. The team is already making code contributions, but Anuff also stressed that Datastax is helping out with scalability testing. “This is one of the things that we learned in our participation in the Apache Cassandra project,” Anuff said. “A lot of what these projects need is folks coming in doing testing, helping with deployments, supporting users. Our goal is to be a great participant in the community.”

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.

Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.

“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”

That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.

Image Credits: Varada

The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the chief software architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).

Image Credits: Varada

Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads, and then caches the data ahead of time, making the system far faster than its competitors.

“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”

That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. As MizMaa’s Aaron Applbaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.

In addition, the system can allocate resources intelligently so that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.

“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applbaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”

Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.

“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.

The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.

“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.

Sherman Ye founded VESoft in 2018 when he saw a growing demand for graph databases in China. Its predecessors, like Neo4j and TigerGraph, had already been growing aggressively in the West for a few years, while China was just getting to know the technology that leverages graph structures to store data sets and depict their relationships, such as those used for social media analysis, e-commerce recommendations and financial risk management.

VESoft is ready for further growth after closing an $8 million funding round led by Redpoint China Ventures, an investment firm launched by Silicon Valley-based Redpoint Ventures in 2005. Existing investor Matrix Partners China also participated in the Series pre-A round. The new capital will allow the startup to develop products and expand to markets in North America, Europe and other parts of Asia.

The 30-people team is comprised of former employees from Alibaba, Facebook, Huawei and IBM. It’s based in Hangzhou, a scenic city known for its rich history and housing Alibaba and its financial affiliate Ant Financial, where Ye previously worked as a senior engineer after his four-year stint with Facebook in California. From 2017 to 2018, the entrepreneur noticed that Ant Financial’s customers were increasingly interested in adopting graph databases as an alternative to relational databases, a model that had been popular since the 80s and normally organizes data into tables.

“While relational databases are capable of achieving many functions carried out by graph databases… they deteriorate in performance as the quantity of data grows,” Ye told TechCrunch during an interview. “We didn’t use to have so much data.”

Information explosion is one reason why Chinese companies are turning to graph databases, which can handle millions of transactions to discover patterns within scattered data. The technology’s rise is also a response to new forms of online businesses that depend more on relationships.

“Take recommendations for example. The old model recommends content based purely on user profiles, but the problem of relying on personal browsing history is it fails to recommend new things. That was fine for a long time as the Chinese [internet] market was big enough to accommodate many players. But as the industry becomes saturated and crowded… companies need to ponder how to retain existing users, lengthen their time spent, and win users from rivals.”

The key lies in serving people content and products they find appealing. Graph databases come in handy, suggested Ye, when services try to predict users’ interest or behavior as the model uncovers what their friends or people within their social circles like. “That’s a lot more effective than feeding them what’s trending.”

Neo4j compares relational and graph databases (Link)

The company has made its software open source, which the founder believed can help cultivate a community of graph database users and educate the market in China. It will also allow VESoft to reach more engineers in the English-speaking world who are well-acquainted with the open-source culture.

“There is no such thing as being ‘international’ or ‘domestic’ for a technology-driven company. There are no boundaries between countries in the open-source world,” reckoned Ye.

When it comes to generating income, the startup plans to launch a paid version for enterprises, which will come with customized plug-ins and host services.

The Nebula Graph, the brand of VESoft’s database product, is now serving 20 enterprise clients from areas across social media, e-commerce and finance, including big names like food delivery giant Meituan, popular social commerce app Xiaohongshu and e-commerce leader JD.com. A number of overseas companies are also trialing Nebula.

The time is ripe for enterprise-facing startups with a technological moat in China as the market for consumers has been divided by incumbents like Tencent and Alibaba. This makes fundraising relatively easy for VESoft. The founder is confident that Chinese companies are rapidly catching up with their Western counterparts in the space, for the gargantuan amount of data and the myriad of ways data is used in the country “will propel the technology forward.”

Companies spend inordinate amounts of time and money building data warehouses and moving data from enterprise applications. But once they get the data in, how do they get specific information like product data back out and distribute it to business operations, which can use it to better understand customers? That’s where Census comes in. It builds a layer on top of the data warehouse that makes it easy for the data team to distribute product data where it’s needed.

The company announced a $4.3 million seed today, although it closed last year while they were still building the product. That round was led by Andreessen Horowitz with help from SV Angel and a number of angel investors.

Census CEO Boris Jabes says the company was founded to solve this problem of data distribution from a cloud data warehouse. He says for starters they are concentrating on product data.

“The product is designed to sync data directly from cloud data warehouses like Snowflake, BigQuery and Redshift […] and the main reason we did that was people really needed to get access to this kind of product data and all this data that’s locked in all their systems and take advantage of it,” Jabes explained.

He says that the first step is to make the product data sitting in the data warehouse actionable for the organization. They are working with data teams at early customers to remove the complexity of getting that data out of the warehouse and putting it to work in a more automated fashion.

They do this by creating a unified schema that sits on top of the data in the warehouse and makes it easier to distribute it to the teams that need it inside the organization. It essentially acts as a middleware layer on top of the warehouse that you can take advantage of without having to write code to decide where data might be most useful.

David Ulevitch, who led the investment at a16z, says that removing this manual part of the process is highly valuable. “For years, organizations have had to do the frustrating task of manually syncing data between dozens of apps. This friction is especially painful now that data has become critical to every team in a business, from product to sales. Census sets a new standard for how product-led SaaS companies can operationalize data,” he said in a statement.

Jabes understands these are difficult times for every business, and especially an early-stage startup, but he says they are focusing on an aspect of the business that potential customers need.

“We’ve seen companies actually spending time trying to tackle some of these data problems […] so I’m still optimistic,” he says.

Couchbase, the Santa Clara-based company behind the eponymous NoSQL cloud database service, today announced that it has raised a $105 million all-equity Series G round “to expand product development and global go-to-market capabilities.”

The oversubscribed round was led by GPI Capital, with participation from existing investors Accel, Sorenson Capital, North Bridge Venture Partners, Glynn Capital, Adams Street Partners and Mayfield. With this, the company has now raised a total of $251 million, according to Crunchbase.

Back in 2016, Couchbase raised a $30 million down round, which at the time was meant to be the company’s last round before an IPO. That IPO hasn’t materialized, but the company continues to grow, with 30% of the Fortune 100 now using its database. Couchbase also today announced that, over the course of the last fiscal year, it saw 70% total contract value growth, more than 50% new business growth and over 35% growth in average subscription deal size. In total, Couchbase said today, it is now seeing almost $100 million in committed annual recurring revenue.

“To be competitive today, enterprises must transform digitally, and use technology to get closer to their customers and improve the productivity of their workforces,” Couchbase President and CEO Matt Cain said in today’s announcement. “To do so, they require a cloud-native database built specifically to support modern web, mobile and IoT applications. Application developers and enterprise architects rely on Couchbase to enable agile application development on a platform that performs at scale, from the public cloud to the edge, and provides operational simplicity and reliability. More and more, the largest companies in the world truly run their businesses on Couchbase, architecting their most business-critical applications on our platform.”

The company is playing in a large but competitive market, with the likes of MongoDB, DataStax and all the major cloud vendors vying for similar customers in the NoSQL space. One feature that has always made Couchbase stand out is Couchbase Mobile, which extends the service to the cloud. Like some of its competitors, the company has also recently placed its bets on the Kubernetes container orchestration tools with, for example the launch of its Autonomous Operator for Kubernetes 2.0. More importantly, though, the company also introduced its fully managed Couchbase Cloud Database-as-a-Service in February, which allows businesses to run the database within their own virtual private cloud on public clouds like AWS and Microsoft Azure.

“We are excited to partner with Couchbase and view Couchbase Server’s highly performant, distributed architecture as purpose-built to support mission-critical use cases at scale,” said Alex Migon, a partner at GPI Capital and a new member of the company’s board of directors. “Couchbase has developed a truly enterprise-grade product, with leading support for cutting-edge application development and deployment needs. We are thrilled to contribute to the next stage of the company’s growth.”

The company tells me that it plans to use the new funding to continue its “accelerated trajectory with investment in each of their three core pillars: sustained differentiation, profitable growth, and world class teams.” Of course, Couchbase will also continue to build new features for its NoSQL server, mobile platform and Couchbase Cloud — in addition, the company will continue to expand geographically to serve its global customer operations.