Databricks, 'the data and AI' company, has a vision to make big data easy so that every organization can turn its data into value. Databricks is a one-stop product for all data needs, from data storage to data analysis, and building predictive models using SparkML. It also provides active connections to visualization tools such as PowerBI, Tableau, Qlikview etc.
Big Data Challenges
Big data improves businesses with advanced science becoming more and more real, but analyzing and processing data is still something hard to manage. Here's a rundown of some of the big data challenges:
- Setting-up and Maintaining Clusters
- Data Pipeline Implementation
- Systems are hard to use
Data engineers and data scientists at both big and small-sized organizations are struggling to set up and maintain clusters. Setting up a cluster is a bit of a tough process. Even if the organization already has an on-premise cluster, it may take 2-3 months to get a few more servers for a new big data project.
Implementing a data pipeline seems like stitching together a hodgepodge of disparate, complex systems, including batch processing systems, query engines, business intelligence tools, etc.
Due to the different APIs and programming languages like (Scala, R, Python), even after setting up the pipeline, the systems are difficult to operate. Also, advanced analytics and building data applications are not easy to perform.
Databricks can address all of these challenges with an end-to-end platform for data analysis and processing that we believe will make big data easier to use than ever before.
Databricks, which is built around Apache Spark, consists of two additional components, a hosted platform (Databricks Platform) and a workspace (Databricks Workspace), to address these challenges.
Apache Spark: Bringing together existing big data platforms
Apache Spark unifies many of the functionalities provided by today's big data systems, and has a single API that supports batch processing, interactive query processing, streaming, machine learning, and graph-based calculations. This allows developers, data scientists, and data engineers to use a single system to implement their whole pipeline.
Databricks Platform: Eliminates the Need for Cluster Maintenance
The Databricks Platform is a hosted platform that makes creating and managing clusters easy. The Databricks Platform contains a powerful cluster manager that allows customers to set up a cluster in seconds and provides them with everything they need right out of the box. Databricks Platform, in particular, provides security and resource separation, as well as a fully configured and up-to-date Spark cluster, dynamic scaling, and smooth data import. Databricks Platform eliminates the need to set up and maintain an on-premise cluster in this manner.
Databricks Workspace: Making Big Data Frameworks Easy-to-Use
Databricks Workspace substantially simplifies the use of big data frameworks, in general, and Spark in particular, by delivering three powerful web-based applications: notebooks, dashboards, and a job launcher.
Notebooks: Currently, notebooks allow users to query and analyze data using Python, SQL, and Scala.
Dashboards: Dashboards are interactive, as every plot can depend on one or more variables. When these variables are updated, the query behind each plot is automatically re-executed, and the plot is regenerated.
Job Launcher: It allows users to launch arbitrary Spark jobs programmatically. Users can, for example, schedule jobs to be executed on a regular basis or when their input changes.
Building Big Data Pipelines with Databricks
Databricks can read data from various AWS storage systems and databases, and an ODBC connector allows customers to use their favorite business intelligence tools.
As a result, Databricks lets customers focus on discovering answers and creating excellent data products rather than fiddling with clustering and sewing together difficult-to-use platforms.
Databricks Support for Third-Party Applications
Databricks also supports some of the third-party applications. Databricks has worked with some of the certified Spark application developers to run their applications on top of Databricks. Databricks is also looking forward to extending Databricks with a vibrant application ecosystem.
Databricks has made large data analysis a lot easier, and it'll soon be the greatest place to build, test, and run data products. This will help the Spark community develop even faster by making large amounts of data more accessible than ever before.