What is Databricks?
Databricks is the first unified analytics engine, that aims to help clients with cloud-based big data processing and machine learning. The team established Databricks who developed Apache Spark, the most active and powerful open source information handling engine designed for advanced analytics, ease of use and velocity. Databricks is the largest contributor to the open-source initiative of Apache Spark that delivers ten times as much software as any other company. More than 40,000 users have also been trained by the company on Apache Spark and have the largest number of customers with Spark deploying and running. It retains a mission to empower individuals and organizations to rapidly create and deploy advanced analytical solutions. And it was done through the virtual analytics platform of Databricks.
Apache Spark – A Brief Note
Apache Spark is an open source cluster computing solution and in-memory processing framework that extends the MapReduce model to support other types of computations such as interactive queries and stream processing. Designed to cover a variety of workloads, Spark introduces an abstraction called Resilient Distributed Datasets (RDDs) that enables running computations in memory in a fault-tolerant manner. RDDs, which are immutable and partitioned collections of records, provide a programming interface for performing operations, such as map, filter and join, over multiple data items. As an advance fault protection, Spark records all transformations carried out to build a dataset, thus forming a lineage graph.
Spark is intended to cover a broad variety of workloads requiring distinct distributed systems, including batch apps, iterative algorithms, interactive queries, and streaming. By supporting these workloads in the same engine, the Apache Spark makes it simple and sensible to combine distinct kinds of information processing that are often required in pipelines (data) for manufacturing assessment. It also decreases the management burden of keeping distinct instruments. Spark is intended to be extremely available, providing easy Python, Java, Scala, and SQL APIs, as well as wealthy built-in libraries. It also tightly integrates with the other Big Data Instruments. In Hadoop c, Spark can also operate.
Challenges Solved By Databricks
Data spread in disparate Silos across the organisation, and the use-cases to generate value from information are becoming more worldly. As the quantity and complexity of information increases, the issue only worsens as it creates the need to provide ideas more quickly. In addition, teams ‘ capacity to prototype and operationalize data-driven solutions is also hampered by fragmented systems and instruments, each with restricted capacities, as well as the failure to use more data science to create smarter choices readily.
As a consequence, information experts face many severe difficulties in bridging the gap between raw data and company value-creating alternatives, including:
- Providing on-scale simple and quick access to information.
- Deploying machine learning and streaming apps of production quality.
- Using more data science to support decision-making.
Providing on-scale simple and quick access to information
Which means processing both structured and unstructured data, ingesting from non-traditional data storages like AWS S3 and others, reducing the batch processing time.
Deploying machine learning and streaming apps of production quality
Setting up, tuning, and scaling Apache Spark clusters for the team. Keeping clusters resilient and up-to-date with the latest versions and scheduling, running, and debugging applications in production.
Using more data science to support decision-making
Which points interactive data exploration & visualization, building real-time dashboards and connecting to Business Intelligence tools.
Databricks Unified Analytics Platform
Databricks have a Unified Analytics Platform (UAP), that accelerates innovation by unifying data science, engineering and business.
By virtualizing storage, Databricks enables access to data anywhere.
- Connect directly to your data stores — no migration required.
- Separate compute from storage — scale each independently as needed.
Orchestrated Apache Spark In Cloud:
Databricks offers a highly secure and reliable production environment in the cloud when supported by Spark experts offering cloud managed services.
- Powerful cluster management capabilities allow you to create new clusters in seconds, dynamically scale them up and down, and share them across teams.
- Intuitive interfaces that enable your teams to use Spark with traditional BI tools such as Tableau Software, or programmatically use the clusters via restful APIs.
- Secure data integration capabilities built on top of Spark so you can unify your data without centralization.
- Instant access to the latest Spark features as with each release.
Through a collaborative and integrated environment, Databricks democratizes and streamlines the process of exploring data, prototyping, and operationalizing data-driven applications in Spark.
- Easy data exploration allows teams to determine what the data lets you do.
- Interactive dashboards empower teams to create dynamic reports.
- A simple and collaborative environment that enables your entire team to use Spark and interact with the data simultaneously.
Custom Spark Applications:
Databricks provides a flexible job scheduler that enables a seamless transition from prototyping to production deployment without incremental work.
- Monitor progress through custom alerts for job completion and failure, and easily view historical and in-progress results.
- Enable production deployments, especially long-running applications such as streaming, to be automatically re-launched whenever failure happens.
Databricks Enterprise Security Framework:
Databricks empowers enterprises with security-enabled data democratization so that they can confidently build advanced analytics solutions when security considerations are paramount.
- Encryption: Provides strong encryption at-rest and in-flight with best-in-class standards such as SSL and keys stored in AWS Key Management System (KMS).
- Integrated Identity Management: Facilitates seamless integration with enterprise identity providers via SAML 2.0 and Active Directory.
- Role-Based Access Control: Enables well-packed management access to every component of the enterprise data infrastructure, including files, clusters, code, application deployments, caching, dashboards, and reports.
- Data Governance: Guarantees the ability to monitor and audit all actions taken in every aspect of the enterprise data infrastructure.
- Compliance Standards: Databricks has successfully completed SOC 2 Type 1 certification and can offer a HIPAA-compliant service. We also plan to achieve security compliance standards that exceed the high standards of FedRAMP as part of Databricks’ ongoing DBES strategy.
Benefits of Databricks
Let us now look at some of the benefits of Databricks,
Make your data stores accessible to anyone in the organization and enable your teams to directly query the data through a “simple-to-use” interface without cumbersome ETL(Extract, Transform, Load) / ELT (Extract, Load, Transform) or Data Warehouse / Data Lake processes. The virtual analytics platform democratizes data access by uncoupling storage from computing and providing infinite scalability, to increase agility and better cost management. With Databricks, you can always get the resources to analyze your data by just scaling up the computer resource in a short burst.
Zero Management Apache Spark
Enable your teams to provide highly available and performance optimized Spark clusters in a self-service fashion, allowing everyone to build and deploy advanced analytics applications with no DevOps expertise. With Databricks, your team will always have access to the latest Spark features so you can leverage the latest innovation from the open source community and focus on your core mission instead of managing the infrastructure. Databricks also offers monitoring and recovery mechanisms that automatically recover clusters from failures without any manual intervention. With Databricks your infrastructure will be fast and secure without any custom work in Spark.
Agile Data Science
Another benefits of Databricks is that it provides an integrated workspace that fosters collaboration through a multi-user environment that allows your team to build new machine learning and streaming applications on top of Spark. Through an interactive notebook environment, you can also create dashboards and interactive reports – allowing everyone to visualize results in real-time, train and tune machine learning models, or easily utilize any of Spark’s libraries to process data. The integrated workspace helps developers and data scientists to reproduce analysis more easily, reuse more code, and simplifies the entire workflow.
Databricks provide unparalleled supports form the leading committers who engineer Apache Spark, and they have support for SQL also.
With this article we discussed the reasons to why use Databricks and some of the key benefits of Databricks. Databricks provides a fast, simple, and scalable way to build a just-in-time data warehouse that eliminates the need to invest in costly ETL pipelines and scales on-demand, revolutionizing the way data teams analyze their data sets. It scales storage and compute resources independently on-demand and support both traditional ETL as well as directly access data to accelerate time-to-insight. Databricks is a higher-level platform that also includes multi-user support, an interactive UI, security, data management, cluster sharing and job scheduling. These qualities make the Databricks different.
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