Databricks

 

Databricks is a unified analytics platform that helps organizations to solve their most challenging data problems. It is a cloud-based platform that provides a single environment for data engineering, data science, and machine learning.

Databricks offers a wide range of features and capabilities, including:

• Apache Spark: Databricks is built on Apache Spark, a unified analytics engine for large-scale data processing.
• Delta Lake: Delta Lake is a unified data lake storage format that provides ACID transactions, version control, and lineage.
• MLflow: MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
• Workspaces: Databricks Workspaces provide a secure and collaborative environment for data scientists and engineers to work together.
• Notebooks: Databricks Notebooks are a powerful tool for data exploration, analysis, and visualization.
• Jobs: Databricks Jobs are a way to automate data pipelines and workflows.
• Monitoring: Databricks provides a comprehensive monitoring dashboard that provides visibility into your data and workloads.

Databricks is a popular choice for organizations of all sizes. It is used by some of the world's largest companies, such as Airbnb, Spotify, and Uber.

Here are some of the benefits of using Databricks:

• Speed: Databricks can help you to process large amounts of data quickly and efficiently.
• Scalability: Databricks is scalable, so you can easily add more resources as your needs grow.
• Ease of use: Databricks is easy to use, even for non-technical users.
• Collaboration: Databricks provides a collaborative environment for data scientists and engineers to work together.
• Security: Databricks is secure, so you can be confident that your data is safe.

If you are looking for a unified analytics platform that can help you to solve your most challenging data problems, then Databricks is a good choice.

Here are some of the use cases for Databricks:

• Data engineering: Databricks can be used to build and manage data pipelines.
• Data science: Databricks can be used to develop and deploy machine learning models.
• Business intelligence: Databricks can be used to create interactive dashboards and reports.
• Regulatory compliance: Databricks can be used to help organizations comply with regulations, such as GDPR and CCPA.
• Research: Databricks can be used to conduct research and analysis on large datasets.

If you are interested in learning more about Databricks, I recommend that you visit the Databricks website.

Data Catalog

 A data catalog is a system that collects and organizes metadata about data assets. It provides a central repository for information about the data, such as its source, format, and usage. Data catalogs can be used to help people find and use the data they need, and to improve the overall management of data assets.

Here are some of the benefits of using a data catalog:

• Improved data discovery: Data catalogs can help people find the data they need by providing a central repository for information about the data. This can save time and effort, and it can help to ensure that people are using the most accurate and up-to-date data.
• Increased data usability: Data catalogs can make data more usable by providing information about the data's format, lineage, and quality. This can help people understand the data and to use it more effectively.
• Improved data governance: Data catalogs can help to improve data governance by providing information about the data's ownership, access control, and security. This can help to ensure that the data is managed in a secure and compliant manner.
• Reduced data duplication: Data catalogs can help to reduce data duplication by providing information about the data's location and usage. This can help to prevent people from creating duplicate copies of the data.
• Improved data quality: Data catalogs can help to improve data quality by providing information about the data's lineage and quality. This can help to identify and correct errors in the data.

There are two main types of data catalogs:

• Enterprise data catalogs: These are designed to be used by entire organizations. They typically store metadata about all of the data assets in the organization.
• Self-service data catalogs: These are designed to be used by individual users or teams. They typically store metadata about the data assets that are relevant to the user or team.

Data catalogs can be implemented using a variety of technologies, such as Hadoop, Hive, and Spark. The best technology for your organization will depend on your specific needs and requirements.

If you are considering implementing a data catalog in your organization, I recommend that you do the following:

• Define your goals: The first step is to define your goals for the data catalog. What do you want to achieve by implementing a data catalog?
• Identify your stakeholders: The next step is to identify your stakeholders. Who will be using the data catalog?
• Assess your current state: The next step is to assess your current state of data management. What are your strengths and weaknesses?
• Develop a plan: The next step is to develop a plan for implementing the data catalog. This plan should include the goals, stakeholders, and resources needed for the data catalog.
• Implement the plan: The next step is to implement the plan for the data catalog. This may involve making changes to your policies, procedures, and technology.
• Monitor and improve: The final step is to monitor and improve the data catalog. This will help you to ensure that the data catalog is effective and that it meets your goals.

By following these steps, you can implement a data catalog in your organization and reap the benefits that it has to offer.

KERBEROS - ACL Example

 Here is an example of a kadm5.acl file:

*/admin@ATHENA.MIT.EDU    *                               # line 1
joeadmin@ATHENA.MIT.EDU   ADMCIL                          # line 2
joeadmin/*@ATHENA.MIT.EDU i   */root@ATHENA.MIT.EDU       # line 3
*/root@ATHENA.MIT.EDU     ci  *1@ATHENA.MIT.EDU           # line 4
*/root@ATHENA.MIT.EDU     l   *                           # line 5
sms@ATHENA.MIT.EDU        x   * -maxlife 9h -postdateable # line 6

(line 1) Any principal in the ATHENA.MIT.EDU realm with an admin instance has all administrative privileges except extracting keys.

(lines 1-3) The user joeadmin has all permissions except extracting keys with his admin instance, joeadmin/admin@ATHENA.MIT.EDU (matches line 1). He has no permissions at all with his null instance, joeadmin@ATHENA.MIT.EDU (matches line 2). His root and other non-admin, non-null instances (e.g., extra or dbadmin) have inquire permissions with any principal that has the instance root (matches line 3).

(line 4) Any root principal in ATHENA.MIT.EDU can inquire or change the password of their null instance, but not any other null instance. (Here, *1 denotes a back-reference to the component matching the first wildcard in the actor principal.)

(line 5) Any root principal in ATHENA.MIT.EDU can generate the list of principals in the database, and the list of policies in the database. This line is separate from line 4, because list permission can only be granted globally, not to specific target principals.

(line 6) Finally, the Service Management System principal sms@ATHENA.MIT.EDU has all permissions except extracting keys, but any principal that it creates or modifies will not be able to get postdateable tickets or tickets with a life of longer than 9 hours.

KERBEROS Cheet Sheet

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