Four key initiatives to simplify your data analysis

2022-06-19 0 By

Today, companies around the world are turning to analytics and machine learning (ML) to better understand their customers — and after accumulating vast amounts of data, they are using this valuable information to build and train machine learning models.However, many companies are not finding it easy, and their data scientists and engineering teams face a number of challenges that make it difficult for them to operate as efficiently as they need.To that end, Joann Starke, an engineer at HPE, proposed four steps to simplify data analytics for data-driven enterprises. Here are excerpts:According to Starke, one of the biggest challenges is that each character needs to use completely different tools to process data.As shown in the figure above, Data Engineers are fetching Data and building a Data pipeline to create join points for the other two roles.As a result, they prefer tools based on open source technologies that can speed up innovation while reducing the lock on proprietary technology stacks.Analytic users navigate the SQL-based world, so they prefer tools like Presto-SQL and Apache Spark on Kubernetes.Such neutral platform tools allow them to deploy any application or framework to any environment and infrastructure.Data scientists also need to build Data pipelines, but they approach them in two different ways:Senior scientists prefer tools like Jupyter Notebook, PyTorch, and Apache Spark, while average citizen scientists prefer to use pre-integrated solution stacks.In addition, Starke said another major challenge is the location of the data.Data centers, clouds, and edges all have different infrastructures and services, as well as specific access paths that can disrupt certain applications or hinder user productivity and access.How can companies address these challenges?Starke outlined four key initiatives: 01. Increasing productivity through a convenient and secure data experience To achieve this, companies don’t have to move data to the same location, just a unified analytics platform.The platform requires a built-in app store that allows different roles in the enterprise to download libraries, preconfigured templates, or certified ISV solutions for their projects.Automate Teams can work faster and more efficiently with Automate everything.Automation should include end-to-end configuration based on tools, libraries, and frameworks.Simplifying Data access The architectural pattern of Data Fabric simplifies Data access by combining different types of Data in multiple locations into a single Data infrastructure, regardless of where the Data is located, reducing complexity by providing direct access to the Data.An enterprise’s data weaving architecture should support files, objects, streams, and databases, as well as the ability to ingest data and transform it into a single, persistent data storage module.Leveraging Open Source to reduce Complexity An open source base allows data science teams to choose and run their work on any infrastructure, whether on-premise, in the cloud, or on the edge.Today, there are a variety of platform tools on the market for data analytics, and HPE has solutions to provide users with a high performance, cost-effective and secure unified data experience.Built based on open source technology, it allows enterprises to quickly migrate to modern analysis platforms without reconstructing or moving data, further improving the work efficiency of data engineers, data analysis users and data scientists.