Twitter and Google Cloud announced they have inked a multi-year partnership that includes using Google's machine learning to improve Twitter's data insights. Twitter is moving its offline analytics, data processing and machine learning (ML) workloads over to Google's Data Cloud as part of its cloud migration strategy.
Twitter first started working with Google Cloud in 2018 to migrate its Hadoop clusters over to Google Cloud Platform as part of its cloud strategy, which is cloud "Partly Cloudy."
The expanded partnership now includes Google Cloud providing a series of data points behind every Tweet, Like and Retweet in order to help Twitter's teams get a deeper understanding of how people are using its platform.
Twitter's data platform swallows trillions of events, processes hundreds of petabytes of data and runs tens of thousands of jobs over a dozen clusters each day. While Twitter is capable of generating tons of data, it tapped Google Cloud to help convert that data into meaningful insights and actionable items.
In order to do just that, Twitter is adopting Google's Data Cloud including BigQuery, Dataflow, Cloud Bigtable and machine learning (ML) tools. Twitter said those tools not only power its rapidly growing data ecosystem to enable faster data-informed decisions, but also to enable deeper ML-driven product innovation.
Google Cloud has been attempting to close ground on cloud giants Amazon Web Services and Microsoft Azure over the past few years. After reporting the cost and operating profit of Google Cloud for the first time on Tuesday during Alphabet's earnings, Google Cloud's revenue grew 47% year-over-year to $3.84 billion, but it posted operating losses of $1.24 billion in the fourth quarter compared to losses of $1.19 billion in the same quarter a year ago.
RELATED: Google Cloud's revenue continues to grow, but losses mount
One of the advantages for using Google Cloud is customers are able to tap into Google's years of in-house expertise for using ML and artificial intelligence. With Google's Data Cloud, Twitter will be able to democratize data access by offering a range of data processing and machine learning tools to better understand and improve how Twitter features are used.
Previously, engineers and data scientists had to develop large custom data processing jobs, which can now be queried faster using SQL in BigQuery. All of which will make it easier for both technical and non-technical teams to study data and accelerate the time to insight.
"Our initial partnership with Google Cloud has been successful and enabled us to enhance the productivity of our engineering teams," said Twitter CTO Parag Agrawal, in a statement. "Building on this relationship and Google's technologies will allow us to learn more from our data, move faster and serve more relevant content to the people who use our service every day. As Twitter continues to scale, we're excited to partner with Google on more industry-leading technology innovation in the data and machine learning space."
RELATED: Ford hitches its cloud wagon to Google for connected vehicles, better supply chains
On Monday, Google and Ford announced a six-year partnership that included naming Google as Ford's preferred cloud provider. In addition to cloud computing, Ford will also tap into Google's artificial intelligence, software and applications such as Google Assistant and Google Maps.
RELATED: Twitter jumps on public cloud with AWS to strengthen its user feeds
In December, Twitter announced it would use Amazon Web Services' public cloud infrastructure to power its real-time workloads and develop new features. Previous to that deal, Twitter had employed its own data centers over the years, but the multi-year deal gave Twitter access to AWS' global cloud infrastructure to deliver its timelines.
RELATED: Google Cloud, AWS and Azure want to 'friend' telcos, but not every telco is ready to click 'accept'
While the competition between AWS, Azure and Google Cloud, among others, is fierce, enterprises and service providers are forging multiple cloud partnerships based on their end customers' demands and needs.