In this article, we’ll explore the benefits of becoming certified in Databricks, the available certification pathways, and tips for preparing for the exam. Don’t miss out on this opportunity to stand out in the job market and advance your career in data!

What is databricks?

Databricks is a cloud-based data analytics and machine learning platform that is built on Apache Spark. It is designed to enable data engineers, data scientists, and other professionals to build and maintain data pipelines, perform data analytics, and build and deploy machine learning models.

The Databricks platform provides a range of tools and features that make it easier to work with big data and machine learning, including:

• A collaborative workspace that allows teams to work together on data projects in real time

• A variety of data connectors that enable users to easily access and integrate data from a range of sources, such as databases, data lakes, and cloud storage

• A variety of machine learning libraries and frameworks that enable users to easily build and deploy machine learning models

• A range of visualization and reporting tools that make it easy to understand and communicate the insights gained from data analysis

In addition to its core platform, Databricks also offers a range of services and solutions that help organizations to optimize their data pipelines, improve their machine learning efforts, and gain insights from their data.

What are the benefits of this tech?

As organisations act on their digital strategy, the data platform provides the core foundation to this these initiatives and specific benefits include;

1. Data engineering: Databricks can be used to build, maintain, and optimise data pipelines, making it easier to process and transform large volumes of data from various sources. This can help organisations improve the efficiency of their data operations and reduce the time and cost of data processing.

2. Data science and machine learning: Databricks can be used to build, train, and deploy machine learning models, as well as to perform data analysis and visualisation. This can help organisations gain insights from their data, improve decision-making, and drive innovation.

3. Customer analytics: Databricks can be used to analyse customer data, such as customer behavior, preferences, and feedback, to better understand and serve customers. This can help organisations improve customer satisfaction, retention, and loyalty.

4. Fraud detection: Databricks can be used to build and train machine learning models to detect fraudulent activity, such as credit card fraud, insurance fraud, and money laundering. This can help organisations reduce the risk of fraud and improve the security of their operations.

5. Supply chain optimisation: Databricks can be used to analyse supply chain data, such as inventory levels, demand patterns, and logistics data, to optimise operations and reduce costs. This can help organisations improve efficiency, reduce waste, and increase profitability.

6. Predictive maintenance: Databricks can be used to build and train machine learning models to predict when equipment is likely to fail, allowing organisations to schedule maintenance before problems occur. This can help organisations reduce downtime, improve equipment utilisation, and reduce maintenance costs.

Example of this include

1. Lyft: Lyft, a ride-hailing company, used Databricks to build a real-time data platform that processes billions of events per day and enables data scientists and engineers to quickly access and analyse data. This has helped Lyft improve the efficiency of its operations and make better-informed business decisions.

2. Mailchimp: Mailchimp, an email marketing platform, used Databricks to build a data platform that enables data scientists and engineers to analyse customer data and improve email marketing campaigns. This has helped Mailchimp improve customer satisfaction and increase revenue.

3. eBay: eBay, an e-commerce company, used Databricks to build a data platform that enables data scientists and engineers to analyse customer data and improve the performance of its website and mobile app. This has helped eBay improve customer satisfaction and increase revenue.

4. Blue Shield of California: Blue Shield of California, a healthcare provider, used Databricks to build a data platform that enables data scientists and engineers to analyse healthcare data and improve the quality of care for its members. This has helped Blue Shield of California improve patient outcomes and reduce costs.

How can I advance my understanding of this tech?

Databricks offers several certification pathways, including:

1. Databricks Certified Associate Developer for Apache Spark: This certification is designed for data engineers and data scientists who have a strong understanding of Apache Spark and are able to use Databricks to build and maintain data pipelines and perform data analytics.

2. Databricks Certified Data Engineer: This certification is designed for data engineers who have a strong understanding of data engineering concepts and are able to design, build, maintain, and optimize data pipelines using Databricks.

3. Databricks Certified Data Scientist: This certification is designed for data scientists who have a strong understanding of machine learning concepts and are able to use Databricks to build, train, and deploy machine learning models.

To earn these certifications, you will need to pass a proctored exam. You can prepare for the exam by taking online courses and hands-on labs offered by Databricks, as well as by reviewing the exam objectives and studying relevant documentation and resources.

How do I prepare for these exams?

1. Online courses and hands-on labs offered by Databricks: Databricks provides a range of online courses and hands-on labs that can help you learn about Apache Spark and how to use it with Databricks. These resources can provide a solid foundation for your exam preparation.

2. Exam objectives and study guide: Databricks provides a list of exam objectives and a study guide that outline the topics that will be covered on the exam. Reviewing these materials can help you focus your study efforts and understand what to expect on the exam.

3. Apache Spark documentation: The Apache Spark documentation is a comprehensive resource that covers the basics of Spark as well as more advanced topics. Reading through the documentation can help you gain a deeper understanding of how Spark works and how to use it effectively.

4. Apache Spark and Databricks resources: There are many online resources, such as blogs, tutorials, and forums, that provide information and guidance on using Apache Spark and Databricks. Reading through these resources can help you learn from the experiences of others and get a better understanding of how these technologies are used in real-world scenarios.

5. Practical experience: Gaining practical experience with Apache Spark and Databricks by working on projects and experimenting with the platform can be an excellent way to prepare for the exam. This will allow you to apply the concepts and techniques you have learned and get a feel for how to use these technologies in a real-world setting.

What are the best blogs and resources to learn more?

1. Databricks blog: The Databricks blog is a good resource for learning about the latest features and best practices in Databricks. It includes tutorials and examples that you can follow to learn how to use the platform effectively.

2. Databricks Academy: Databricks Academy is a free online learning platform that provides a variety of self-paced courses on Databricks and Apache Spark. They offer courses for beginners as well as more advanced users.

3. Databricks YouTube channel: The Databricks YouTube channel has a variety of videos that cover a range of topics related to Databricks and Apache Spark. They have playlists specifically designed for beginners, which you can use to learn the basics of the platform.

4. Microsoft Learn: Microsoft Learn is a free, online learning platform that provides a variety of courses and modules on a variety of topics, including Databricks.

5. Analytics Roundtable, head over to our growing slack channel to talk to me and other experts for free.

What does the future look like?

Databricks is a leading provider of cloud-based data analytics and machine learning platforms. The company’s platform, which is built on Apache Spark, is widely used by data engineers, data scientists, and other professionals to build and maintain data pipelines, perform data analytics, and build and deploy machine learning models.

Looking to the future, it is likely that Databricks will continue to be a key player in the data analytics and machine learning space, as organisations increasingly rely on data-driven decision making and seek to leverage the power of machine learning. The company is likely to continue to innovate and expand its platform to meet the evolving needs of its customers.

In addition, as the demand for data professionals with expertise in using Databricks and Apache Spark continues to grow, it is likely that the demand for Databricks certifications will also increase. This could provide additional opportunities for professionals who are looking to advance their careers and demonstrate their expertise in using these technologies.

More to come!

I will be posting regularly so stay tuned. If you want additional content, check out Analytics Roundtable, to stay up to date with the latest technology and chat with others.