Databricks is a Cloud-based, industry-leading Data Engineering tool used to process and transform extensive amounts of data and explore it through Machine Learning models. It allows organizations to quickly achieve the full potential of combining their data, ETL processes, and Machine Learning. However, knowing the precise sequence of activities that affect a company’s specific operation, procedure, or event is essential for tightening the security around data.
Audit Logging (or event logging or system logging) creates an Audit Trail, a security-relevant chronological set of records. It documents an organization’s day-to-day digital footsteps, creating detailed records of daily activities.
Audit Logging gives IT administrators visibility into Employees’ Actions and helps to keep that company more secure. For example, event logs act as a detective control because every activity’s trails provide evidence if a hacker or user engages in unauthorized activity.
This article will serve as a guide to monitoring Databricks Logs. You will walk through the basics of Databricks, Data Lakes, Delta Lakes, ETL process, and the process of auditing Databricks Logs.
Table of Contents
- What is Databricks?
- What is a Databricks Workspace?
- What are Databricks Audit Logs?
- Understanding of the need for Audits.
What is Databricks?
Databricks, developed by the creators of Apache Spark, is a Web-based platform, which is also a one-stop product for all Data requirements, like Storage and Analysis. It can derive insights using SparkSQL, provide active connections to visualization tools such as Power BI, Qlikview, and Tableau, and build Predictive Models using SparkML. Databricks also can create interactive displays, text, and code succinctly and tangibly.
Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform, making it easy for businesses to manage a colossal amount of data and carry out Machine Learning tasks.
1) Database Workspace
An Interactive Analytics platform that enables Data Engineers, Data Scientists, and Businesses to collaborate and work closely on notebooks, experiments, models, data, libraries, and jobs.
2) Databricks Machine Learning
An integrated end-to-end Machine Learning environment that incorporates managed services for experiment tracking, feature development and management, model training, and model serving.
With Databricks ML, you can train Models manually or with AutoML, track training parameters and Models using experiments with MLflow tracking, and create feature tables and access them for Model training and inference.
You can now use Databricks Workspace to gain access to a variety of assets such as Models, Clusters, Jobs, Notebooks, and more.
3) Databricks SQL Analytics
A simple interface with which users can create a Multi-Cloud Lakehouse structure and perform SQL and BI workloads on a Data Lake. In terms of pricing and performance, this Lakehouse Architecture is 9x better compared to the traditional Cloud Data Warehouses.
It provides a SQL-native workspace for users to run performance-optimized SQL queries. Databricks SQL Analytics also enables users to create Dashboards, Advanced Visualizations, and Alerts. Users can connect it to BI tools such as Tableau and Power BI to allow maximum performance and greater collaboration.
4) Databricks Integrations
Databricks integrates with a wide range of developer tools, data sources, and partner solutions.
- Data Sources: Databricks can read and write data from/to various data formats such as Delta Lake, CSV, JSON, XML, Parquet, and others, along with data storage providers such as Google BigQuery, Amazon S3, Snowflake, and others.
- Developer Tools: Databricks supports various tools such as IntelliJ, DataGrip, PyCharm, Visual Studio Code, and others.
- Partner Solutions: Databricks has validated integrations with third-party solutions such as Power BI, Tableau, and others to enable scenarios such as Data Preparation and Transformation, Data Ingestion, Business Intelligence (BI), and Machine Learning.
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- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
What is a Databricks Workspace?
Databricks Data Science & Engineering or simply called Workspace is an environment that offers access to Databricks Assets and Streamlined Workflows. The Workspace also acts as a collaborative Analytics platform for Data Scientists, Data Engineers, and Machine Learning Engineers.
It organizes objects like notebooks, libraries, and experiments into folders and provides computational resources such as Clusters. Users can manage the Workspace using the Databricks CLI, Workspace UI, and the Databricks REST API reference.
Databricks Workspace comprises essential elements that help you perform Data Science and Data Engineering tasks.
8 Key Databricks Assets
Below are Databricks Assets available in the Databricks environment:
- Databricks Cluster: It comprises a set of computation resources and configurations to run various Data Engineering and Data Science use cases like — ETL Pipelines, Streaming Analytics, and Machine Learning.
- Databricks Notebooks: It is a Web-based interface that contains a series of commands that operate on files, tables, narrative text, and plot visualizations. Users can run commands in sequence to get desired output from one or more previously run commands.
- Databricks Jobs: Like Databricks Notebooks, the Job is used for running code in the Databricks Cluster. A Job is a non-interactive code created to perform a particular set of tasks interactively or on schedule.
- Databricks Libraries: It helps you install required dependencies to run third-party or custom codes available in your notebooks and jobs running in a Cluster. Users can write a library in Python, Java, Scala, and R. It can be uploaded to external packages like PyPI, Maven, and CRAN repositories. Based on the application, these libraries can be installed in three modes — Workspace, Cluster, and Notebook.
- Databricks Data: It imports desired data into a Distributed File System (DFS) mounted on the Databricks Workspace. Databricks Data is used to work with Databricks Notebooks and Clusters to perform Big Data Analysis and ML tasks.
- Databricks Repos: It is a Databricks folder whose content can be synced with Git repositories. With Databricks Repos, you can develop notebooks in Databricks and use remote Git repositories for collaboration and version control.
- Databricks Models: It refers to a Model registered in the MLflow Model Registry that enables users to manage the entire lifecycle of MLflow Models. Model Registry provides Chronological Model Lineage, Stage Transitions, Model Versioning, and Email notifications of Model Events.
- Databricks Experiments: It is the primary unit of organization and access control for MLflow machine learning model training. Each experiment allows a user to visualize, search, compare runs, and download run metadata for analysis in other tools.
What are Databricks Audit Logs?
1) ETL Process
ETL (extract, transform, and load) is a Data Integration process that performs the Extract, Transform, and Load functions from multiple data sources to a Data Warehouse or a unified Data Repository.
In the 1970s, when Databases grew in popularity, this process was introduced for integrating data for computation and analysis. Today, the ETL process provides the foundation for Machine Learning and Data Analytics workstreams.
Through a series of rules, ETL cleans and organizes data to address specific Business Intelligence requirements like improving back-end processes, end-user experience, or monthly reporting.
2) Delta Live Tables (DLT)
With Delta Live Tables (DLT), users can quickly build and manage reliable Data Pipelines that deliver high-quality data on Delta Lake. DLT helps users simplify ETL development and management with Automatic Data Testing, Declarative Pipeline Development, and Deep Visibility for monitoring and recovery.
- Quickly Build & Maintain Data Pipelines: With DLT, Data Engineering teams don’t have to stitch together siloed data processing jobs manually. Instead, they can easily define end-to-end Data Pipelines by specifying the data’s transformation logic, the source, and the destination state.
- Automatic Testing: DLT helps ensure accurate and useful BI, Data Science, and Machine Learning with high-quality data for downstream users. It employs validation and integrity checks to prevent bad data from flowing into tables. DLT also has predefined error policies to avoid Data Quality errors (fail, drop, alert, or quarantine data).
- Deep Visibility for Monitoring & Easy Recovery: With DLT, you gain deep visibility into Pipeline operations with tools to track operational stats and data lineage visually. They also reduce downtime with Automatic Error Handling and Speed up Maintenance with single-click deployment and upgrades.
3) Audit Log ETL Design
Databricks is Cloud-native by design and is tightly coupled with Microsoft Azure and Amazon Web Services. The Databricks Logs capability in Databricks provides administrators with a centralized way to understand and govern activities performed by Databricks users.
Team administrators use Databricks Logs to monitor patterns like the number of Jobs in a given day, users who did those actions, and users who were denied authorization into the workspace.
The primary purpose of Databricks Logs is to allow platform administrators and enterprise security teams to track access to Workspace and Data Resources using the various interfaces available in the Databricks platform.
- Bronze: The initial landing zone of the Data pipeline for Databricks Logs.
- Silver: The raw data gets cleansed, transformed, and potentially enriched with external datasets for Databricks Logs.
- Gold: Production-grade data that your entire company can rely on for Descriptive Statistics, Business Intelligence, and Data Science/Machine Learning for Databricks Logs.
A) Databricks Logs: Raw Data to Bronze Table
Databricks uses a File-based Structured Stream to deliver raw JSON files to a Bronze Delta Lake table, creating a Durable copy of raw data. A durable copy allows users to replay ETL if there is an issue in downstream tables.
The Databricks Logs are delivered to a customer-specified AWS S3 Bucket as JSON. This process uses Structured Streaming to Write-ahead Logs and Checkpoints rather than Writing Logic to determine the state of our Delta Lake tables.
B) Databricks Logs: Bronze to Silver Table
The Data Streams from a Bronze Delta Lake table to a Silver Delta Lake Table. It takes the sparse requestParams StructType and strips out all empty keys for every record, along with performing some other fundamental transformations like parsing email address from a nested field and parsing UNIX epoch to UTC timestamp.
C) Databricks Logs: Silver to Gold Table
The Gold Audit Log tables are the end-results used by Databricks Logs administrators for their analyses. With Databricks Delta Lake’s ability to handle schema evolution gracefully while tracking additional actions for each resource type, the Gold tables will seamlessly update & eliminate the need to check for errors.
In this article, we have covered Databricks Dashboards, Delta Tables, and Databricks Logs. Databricks has become a Big Data Analytics solution that can be easily paired with Cloud solutions like Microsoft, Google, and Amazon. It offers superior log details to monitor patterns like the number of Clusters in a given day, track users who performed those actions, and keep a check on users who were denied authorization.
These features of Databricks can be collectively used to perform many advanced operations like Deep Learning and End-to-end Application Development.
Apart from the data on the Cloud Storage, business data is also stored in various applications used for Marketing, Customer Relationship Management, Accounting, Sales, Human Resources, etc. Collecting data from all these applications is of utmost importance as they provide a clear and deeper understanding of your business performance.
However, your Engineering Team would require to continuously update the connectors as they evolve with every new release. All of this can be effortlessly automated by a Cloud-Based ETL tool like Hevo Data.Visit our Website to Explore Hevo
Hevo Data is a No-code Data Pipeline that assists you in seamlessly transferring data from a vast collection of sources into a Data Lake like Databricks, Data Warehouse, or a Destination of your choice to be visualized in a BI Tool. It is a secure, reliable, and fully automated service that doesn’t require you to write any code!
If you are using Databricks as a Data Lakehouse and Analytics platform in your business and searching for a stress-free alternative to Manual Data Integration, then Hevo can effectively automate this for you. Hevo with its strong integration with 100+ Data Sources & BI tools (Including 40+ Free Sources), allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready.
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