Let's Talk - "Databricks", becoming an integral part of Data Science

Data is everywhere and is being generated at a breakneck pace. This is creating a massive opportunity for organizations to harness data science to transform their business in new ways.

Data science depends a lot on strong data engineering to outfit reliable data. Businesses are generating data at a faster pace than ever: 90% of the world’s data was generated within the last two years. The increased data volume is rapidly outpacing our ability to consume it. Data science allows businesses to efficiently predict future outcomes, and even preemptively take action, based on insights from terabytes of business data. However, as the data continues to grow in volume, new challenges arise that can impede time-to-insight and innovation:

• Spending too much time maintaining infrastructure rather than the data
• Complexity and cost to train machine learning models at scale
• Poor collaboration among team members and across the organization

By combining big data with data science techniques such as machine learning and deep learning, businesses can build and train scalable models that drive new and extraordinary business use cases.

Better Analytics with Databricks

Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. With Databricks, data scientist can securely and reliably deploy production data pipelines with ease.

Automated Infrastructure

Databricks’ serverless and highly elastic cloud service is designed to remove operational complexity while ensuring reliability and cost efficiency at scale, so you can focus on your data instead of DevOps. Through the first serverless API for Apache Spark, organizations can remove the barriers of infrastructure for both end-users and DevOps.

• AUTO-CONFIGURATION - The Spark version deployed in serverless pools is automatically optimized for interactive SQL and Python workloads
• ON-DEMAND ELASTICITY - Databricks automatically scales the compute and local storage resources in the serverless pools in response to Apache Spark’s changing resource requirements for user jobs.
• RELIABLE FINE-GRAINED SHARING - Serverless pools embed preemption and fault isolation into Spark, enabling a pool’s resources to be shared among many users in a fine-grained manner without compromising on reliability

Accelerate Innovation with Collaborative Data Science

Databricks provides an interactive workspace that eliminates the need to integrate third party tools and libraries. Support for multiple programming languages (R, Python, Scala, and SQL) ensures you use the right tool for the job. Improve team productivity by enabling team members to collaborate on the data and models in real time, while tracking usage through viewer logs and revision history

• COLLABORATIVE WORKSPACE - Speed up iterative model building and tuning with interactive notebooks purpose-built to instill collaboration across teams.
• SUPPORT FOR MULTIPLE PROGRAMMING LANGUAGES - Interactively query large-scale data sets in R, Python, Scala, or SQL.
• BUILT-IN VISUALIZATIONS - Visualize insights through a wide assortment of point-and-click visualizations. Or use powerful scriptable options like matplotlib, ggplot, and D3.
• HIGHLY EXTENSIBLE - Make use of popular libraries within notebook or job such as scikit-learn, nltk ML, pandas, etc.

Databricks Platform takes the complexity out of data science at scale, allowing data scientists of all backgrounds and levels of experience to tap into the power of advanced analytic techniques such as machine learning and deep learning. 

Learning Databricks will give the data scientist an extra edge to manage big data easily through the Automated Cluster Management.