Updated April 6, 2023
Definition of Databricks
Databricks is an integrated data analytics tool, developed by the same team who created Apache Spark; the platform meets the requirements of Data Scientists, Data Analysts, Data Engineers in deploying Machine learning techniques to derive deeper insights into big data in order to improve productivity and bottom line; It had successfully overcome the inability of the local warehouses in managing unstructured formats of a high volume of data generated from everywhere; Performance/reliability issues of the old data lake solutions were also addressed in this platform; Three large cloud crowns Amazon warehouse services (AWS), Microsoft Azure and Google cloud platform (GCP) have adopted Databrick in their cloud offerings.
Brief on Databricks
The founders of this platform were the owners of many other Open source big data platforms like ML Flow, Delta Lake, Koalas, and Spark. This product was spawned out of the AMPLab project in California University by a team of academicians and built on top of Scala. The primary aim of this product is to offer a reliable data lake to data-hungry applications.
Apart from the initial funding by its founder, it got a major fund from Microsoft in 2019.
Collaborative workspaces, Managed Infrastructure, Spark, and Delta are its core components.
Databricks Interview Questions and Answers
1. What is Databricks in short (in a sentence)?
A cloud-based Big data platform to manage data lakes and crunch it through Machine learning techniques and get great insights from it.
2. Who are benefited most from Databricks?
Databricks serves Data Scientists, Data Analysts and Data Engineers to derive maximum insights from big data.
3. What are the components of Databricks?
- Workspace for developers to code collaboratively in real-time securely.
- Managed Clusters to scale up the query speed.
- Spark Engine to manage in-memory data processing
- Delta to overcome the shortcomings in conventional data lake file formats
- ML Flow to overcome challenges in production rising ML lifecycle
- SQL Analytics to develop queries to extract data from data lakes and publish it in dashboards.
4. What are the languages supported by Databricks?
R, Python, Scala, Standard SQL, and Java. It also supports several language APIs like SparkR or SparkylR, PySpark, Spark SQL, Spark.api.java.
5. What is the difference between data warehouses and Data lakes?
Data Warehouse mostly contains processed structured data required for business analysis and is managed in-house with local skills. Its structure cannot be changed so easily. Data lakes contain all data including raw and old data, and all types of data including unstructured, can be scaled up easily and the data model can be changed quickly. It is maintained by third-party tools preferably in the cloud and it uses parallel processing in crunching the data.
6. Is there no on-premises option for Databricks and is it available only in the cloud?
Yes. Apache Spark, the base version of Databricks was offered in an on-premises solution and in-house Engineers could maintain the application locally along with the data. Databricks is a cloud-native application and the users will face network issues in accessing the application with data in local servers. Data inconsistency and workflow inefficiencies are the other factors weighed against the on-premises options for Databricks.
7. What are the main types of cloud services?
1. Infrastructure as a service (IaaS)
It’s the first logical step in the cloud journey. Computer hardware, network is hired from a cloud vendor and the entire application environment including the development/ hosting of applications have to be managed by the end consumers.
2. Software as a service (SaaS)
Infrastructure and application environment are provided by cloud vendors and the consumer will have to manage application settings and user authentication only.
3. Platform as a service (PaaS)
Infrastructure and Software development platforms are provided by cloud vendors and consumers will have to configure application settings, develop applications and host them in the cloud.
4. Serverless Computing
It’s an improvised version of PaaS. Server scalability as the application grows is handled by cloud vendors and users don’t have to worry about it.
8. Is Microsoft the owner of Databricks?
No. Databricks is still an open-sourced product built on Apache Spark. Microsoft has made an investment of $250M in 2019. Microsoft integrated some of the services of Databricks into its cloud product Azure and released Azure Databricks in 2017. Similar tie-ups are in place with Amazon cloud AWS and Google cloud GCP.
9. What is the difference between Databricks and Azure Databricks?
Databricks unified Apache Spark’s processing power of data analysis and ML-driven data science/ Engineering techniques in managing the entire data lifecycle from the ingestion state up to the consumption state.
Azure Databricks combines some of Azure’s capabilities along with the analytics features of Databricks to offer the best of both worlds to the end user. It uses Azure’s own data Extraction tool, Data Factory for culling out data from various sources and combines with AI-driven Databricks analytics capability in Transformation and Loading. It also uses MS active directory integration features to gain authentication and other Azure and general features of MS to improve productivity.
10. What is the category of Cloud service offered by Databricks? Is it SaaS or PaaS or IaaS?
The service offered by Databricks belongs to the Software as a service (SaaS) category and the purpose is to exploit the powers of Spark with clusters to manage storage. The users will have to change just the application configurations and start deploying them.
11. What is the category of Cloud service offered by Azure Databricks? Is it SaaS or PaaS or IaaS?
The service offered by Azure Databricks belongs to the Platform as a service (PaaS) category. It provides an application development platform with capabilities built from Azure and Databricks. The users will have to design and develop the data life cycle and develop applications using the services offered by Azure Databricks.
12. Compare Azure Databricks and AWS Databricks
Azure Databricks is a well-integrated product of Azure features and Databricks features.
It’s not a mere hosting of Databricks in the Azure platform. MS features like Active directory authentication and integration of many Azure functionalities make Azure Databricks as a superior product. AWS Databricks is a mere hosting Databricks on AWS cloud.
Conclusion – Databricks Interview Questions
The advent of smartphones and high bandwidth internet availability paving way for building new-generation applications and they are hosted in Cloud by default. Databricks will aid and accelerate such developments to a faster level.
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