![]() Today, Spark uses Snowflake as its data lake and data warehouse for their structured and semi-structured data. Spark leverages Snowflake for a range of use cases. The analysts’ work ranges from reporting to building new analytical and machine learning models. This includes teaming analysts and data scientists with the company’s customer “tribe” to improve marketing campaigns, customer engagement, and products. To improve the company’s agility and foster collaboration, Langham has embedded his data engineers and architects into teams from other parts of the business, spurring data democratization. Today, approximately 110 people at Spark regularly use Alation to find and interpret data stored in Snowflake and the remaining data in its on-premises systems. “When we looked at the features that were available, the compatibility with Snowflake and, obviously, the fact that we were existing Alation users and were already getting a lot of value out of it, it was a fairly obvious choice for us to continue with Alation,” says Langham. Harnessing Data for InsightĪs Spark began their Snowflake migration, they briefly evaluated other compatible data catalogs but chose to stick with Alation. They chose the Microsoft Azure cloud computing service, in part for its compatibility with a number of Microsoft solutions already in place at Spark. This stands in stark contrast to the siloed data landscape of the past, where a separate data warehouse, a separate data lake, and an analytics appliance accommodated different uses-but slowed productivity.įinally, Spark wanted to run Snowflake on a public cloud. All the data now sits on a common technology platform. Spark then evaluated various cloud data platforms and chose Snowflake because it offered genuine separation of compute and storage, and the performance and scalability they needed. Alation brings the metadata and business understanding from all of Spark’s different systems together. This empowers their data engineers and analysts to derive insight from customer and other company data, no matter where it’s located. Spark chose the Alation Data Catalog to promote faster data search and discovery. He wanted to democratize that data, making it available to business units across the organization so they, too, could derive insights to support the company’s strategic goals. ![]() He also believed that data access should not be limited to his data science team. For these reasons, Spark decided to migrate their data to the cloud.įor Langham, an ideal solution would help Spark bring together the required data in the cloud and build advanced analytical models that derive customer insight. Leaders saw that some of their on-premises systems would soon need to be upgraded or replaced, and that scaling their Hadoop cluster would be time consuming and costly. The second challenge, common to any B2C company today, was the massive increase in customer and other data that would only grow with the advances in 5G and internet of things (IOT) technology. It has enabled them to get up and running on Snowflake a lot quicker than they otherwise would have been able to do."ĭomain Chapter Lead – Data Engineering, Spark New Zealand ![]() "Alation has absolutely accelerated the adoption of Snowflake amongst our users. Langham knew that Spark needed a solution to help them find data faster. Even their most highly trained and experienced analysts and engineers spent the majority of their time searching for data-rather than using it. They had an on-premises IBM Db2 enterprise data warehouse (EDW) and a more recently implemented data lake on a Hadoop cluster used for AI and machine learning workloads. In this vast and complex data landscape, Langham’s team had little visibility into where to find the right data for analysis. First, numerous data systems across the company served different and sometimes overlapping purposes. He believes that new types of analytics and insights, such as AI and machine learning, are particularly critical for gaining customer insight.īut before Spark could use data effectively for customer insight, they faced two critical challenges. Use customer insights to deliver the right products at the right timeĭeliver a smart, automated network with advances in 5G and internet of things (IOT) technologyĪs the Domain Chapter Lead for Data Engineering at Spark, Peter Langham knows that data is a critical component to the success of these three strategic pillars. Today, a three-pronged, data-driven strategy propels them into the future:Ĭreate a simple, intuitive customer experience and digital journey They have become the leading communications provider in the country through continuously striving for excellence and improvement. Spark boasts 2.5 million mobile subscribers-in a country of around five million people.
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