Realising The Value of Data
Speed to insight can be the leading source of competitive advantage for any data-driven organisation. It’s the ability to gain real-time access to powerful insights and action them that’s critical to business success.
Gone are the days when getting a data platform in place takes months or even years and requires time, patience and architectural expertise. Cloud technology gives businesses greater flexibility and agility into the process of taking data and transforming it into business insights and actions.
As machine learning and artificial intelligence continue to dominate the tech scene, realising the value of data quickly is paramount. Rapidly connecting to a range of different data sources allows data scientists to collect a cohort of valuable data that can be used as features later in the machine learning life cycle.
Let’s give this some context:
In healthcare, the ongoing COVID-19 pandemic highlights the importance of immediately accessing real-time data and responding swiftly. The purpose of the COVID-19 track and trace app is to locate people and alert them of the need to self-isolate, much faster and more efficiently than traditional methods. While we don’t know the exact technologies that are being utilised under the hood, we can say with a high certainty that processing these data feeds are crucial to its success.
These problems are, of course, not unique to healthcare. Speed matters in all industries, and many enterprises struggle to quickly access insights from their data and gain an advantage over the competition. So how can we tackle these issues? How do I access my data as quickly and effectively as possible?
Over the last few years, Apache Spark has grown rapidly to become one of the key distributed frameworks in the data world. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at massive scale, collectively processing multiple petabytes of data. However, many enterprises often struggle to operationalise Spark in-house.
Using Databricks, businesses can run Apache Spark in a collaborative, scalable and easy-to-use platform for data science and engineering. Databricks enables data teams to unlock quick insights from unstructured and semi-structured data without hardware to build or applications to string together. As a fully managed cloud service, Azure Databricks takes advantage of the elasticity and performance of the cloud. Clusters scale up and down automatically without the need for any complex setup from users. Managing these clusters and ensuring production-level stability is not simple, but Databricks makes Big Data straightforward by providing Apache Spark as a hosted solution.
Starting with a single click on a cloud platform, you can easily launch a Databricks Workspace, spin up a fully managed Spark cluster and connect to a range of data sources in minutes. With collaborative notebooks, Databricks allows you to easily query and analyse silos of data together, using a range of languages including Python, Scala and SQL. This data can be visualised using out of the box notebook charts, as well as connecting to leading Business Intelligence software, such as Tableau, Power BI or Qlik.
Azure Databricks is the perfect technology to deliver insights from big data at the speed of curiosity. It has everything you need to drive innovation in a unified analytics platform.
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