Power BI Data Lake: A Comprehensive Guide to Data Architecture and Design

A Power BI data lake is a centralized repository that stores raw, unprocessed data from various sources, allowing for flexible and on-demand analysis. This architecture is designed to handle large volumes of data from different formats and structures.

Data is typically ingested into a data lake through various means, such as data streaming, file uploads, or database connections. The data is then stored in a format that allows for easy access and querying.

A key benefit of a Power BI data lake is its ability to support self-service analytics, enabling business users to explore and analyze data without relying on IT. This leads to faster time-to-insight and improved decision-making.

Data governance is also a crucial aspect of a Power BI data lake, ensuring that data is accurate, secure, and compliant with regulatory requirements. This involves implementing data quality checks, access controls, and auditing mechanisms.

To set up a Power BI data lake, you'll first need to create an Azure Data Lake Storage Gen2 (ADLS Gen2) instance. This can be done using the Azure CLI with a command that enables hierarchical namespace, making it an ADLS Gen2.

To connect Power BI to your ADLS Gen2 instance, you can use the Azure Storage Explorer to upload files to the storage account. Once the files are uploaded, you can create a file system and select the files you want to upload.

You can configure your Power BI workspace to store dataflow definition and data files in CDM folders in Azure Data Lake. This allows you to attach CDM folders created by other services to Power BI as dataflows, and create datasets, reports, dashboards, and apps using dataflows created from CDM folders in Azure Data Lake.

To set up Power BI with a Data Lake, start by creating a storage account in Azure. Enable hierarchical namespace to make it ADLS Gen2.

First, create a resource group in the Azure portal. Then, create a storage account within that resource group. Make sure to enable Data Lake Storage Gen2 in the storage account settings.

Next, create a file system within your storage account. This will be the location where you'll upload your data files. You can do this by clicking on the File systems menu and then the Plus button to create a new file system.

To upload files to your Data Lake, use the Azure Storage Explorer. Select the file system you created and click Upload. Then, select the files on your local machine to upload to the Data Lake.

With your ADLS Gen2 storage account set up and files uploaded, you're ready to move on to the next step in setting up Power BI with your Data Lake.

Power BI customers can now connect an Azure Data Lake Storage Gen2 account to Power BI, making it easier to store and analyze data.

To get started, all you need is an Azure Data Storage account. This connection allows you to configure workspaces to store dataflow definition and data files in CDM folders in Azure Data Lake.

You can also attach CDM folders created by other services to Power BI as dataflows, and create datasets, reports, dashboards, and apps using dataflows created from CDM folders in Azure Data Lake.

To connect PowerBI to Dremio, open PowerBI Desktop and find the Get Data button on the External data group. Select the Database tab and then find Dremio among the list of supported sources.

Click the Connect button and specify the IP address of the Dremio cluster, such as localhost if it's deployed on your local machine. Select DirectQuery as the connectivity mode to generate new queries to Dremio for each action in PowerBI.

To complete the connection, select the data you want to import from the Dremio space and dataframe, and click the Load button.

To create an ADLS Gen2 service client, you'll need to use the DatalakeServiceClient class from the azure.storage.filedatalake library.

The DatalakeServiceClient class is used to create a service client object using the storage account URL and AD credentials.

You'll need to generate AD credentials previously to use them for this step.

The function that creates and returns a service client object is a crucial part of the process.

You can find the function in the example code that uses the storage account URL and AD credentials.

Storage account URL and AD credentials are essential for creating the service client object.

You can find the access keys for your storage account on the Azure portal by clicking on the Access keys option in your storage account menu.

The shared access key is one of the parameters you'll need to enter when connecting Dremio and ADLS Gen2.

A data lake is a centralized repository designed to hold vast volumes of data in its native, raw format. This flexibility makes it easier to accommodate various data types and analytics needs as they evolve over time.

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. A data lake stores data before a specific use case has been identified.

A data lake utilizes a flat architecture, unlike traditional hierarchical structures and predefined schemas used in data warehouses. This structure is made efficient by data engineering practices that include object storage.

Data lakes are designed to handle petabytes of information that continually flow in from various data sources. They are a versatile platform for exploring, refining, and analyzing this data.

Here are some types of organizations that commonly benefit from data lakes:

Data ingestion is the process of importing data into the data lake from various sources, serving as the gateway through which data enters the lake, either in batch or real-time modes.

Batch ingestion is a scheduled, interval-based method of data importation, often using tools like Apache NiFi, Flume, and traditional ETL tools like Talend and Microsoft SSIS.

Real-time ingestion immediately brings data into the data lake as it is generated, crucial for time-sensitive applications like fraud detection or real-time analytics, often utilizing tools like Apache Kafka and AWS Kinesis.

Data ingestion often utilizes multiple protocols, APIs, or connection methods to link with various internal and external data sources, ensuring a smooth data flow, catering to the heterogeneous nature of the data sources.

Once data is ingested, it can be stored in a data lake, such as Azure Data Lake Storage, which can be connected to Power BI, allowing users to configure their workspaces to use the Azure storage account for dataflow storage.

To store dataflows in Azure Data Lake Storage, workspace admins can turn on dataflow storage setting, which will store dataflow definition and data files in the organization’s Azure Data Lake Storage account.

Your organization's Azure Data Lake Storage account is the perfect place to store your dataflow data. To get started, your administrator needs to connect an Azure Data Lake Storage account to Power BI.

Power BI administrators can then allow users to configure their workspaces to use the Azure storage account for dataflow storage. Once configured, workspace admins can turn on dataflow storage to store dataflows in your organization's Azure Data Lake Storage account.

Dataflow definition and data files will be stored in the Azure Data Lake Storage account, making it easy to manage and access your data. This storage solution is especially useful for large datasets, as it allows for scalable storage and processing.

By storing dataflows in your organization's Azure Data Lake Storage account, you can take advantage of the Common Data Model (CDM) folders, which contain schematized data and metadata in a standardized format. This makes it easier to collaborate and share data across different services and teams.

With CDM folders, authorized Power BI users can build semantic models on top of their data, making it easier to gain insights and make informed decisions. Plus, dataflows created in Power BI can be easily added to the data lake, allowing for seamless integration and analysis.

To make the most of your data lake, consider using the ELT paradigm, which involves extracting data from sources, loading it into the data lake, and then transforming it into a more analyzable form. This approach can help you get the most out of your data and make it easier to work with.

By following these best practices and using the right tools, you can create a robust and scalable data lake that meets the needs of your organization.

Connecting ADLS Gen2 to Dremio is a straightforward process. To start, click on the Add Source button on the Dremio GUI homepage.

Select the Azure Storage source from the options available. This will allow you to connect Dremio to your Azure Storage account.

To authenticate the connection, you'll need to enter the Azure storage account name, account kind, and shared access key. The access keys can be found on the Azure portal by clicking on the Access keys option in your storage account menu.

Security is a top priority when it comes to storing and managing big data in a Power BI data lake. Azure Data Lake Store provides a flexible and secure environment to house even the most extensive datasets.

To ensure the security of your data lake, you can implement access control lists (ACLs) in Azure Data Lake Storage Gen2, which offer more fine-grained permissions than other options.

Governance is also essential for a data lake, establishing and enforcing rules, policies, and procedures for data access, quality, and usability. This ensures information consistency and responsible use, and tools like Apache Atlas or Collibra can add this governance layer.

To assign write permissions to the Azure Function app, you need to give it the necessary permissions to read and write data from the data lake. You can do this by creating a storage container in the ADLS Gen2 storage account using the Azure CLI command.

There are two approaches to assign permissions: using Azure Storage RBAC roles or ADLS Gen2 Access Control Lists. For the sample app, we'll use the Azure Storage RBAC role Storage Blob Data Contributor.

You can use the following Azure CLI command to assign this role: This will allow the function app to read and write data from the container. The command is:

A crosscutting governance and security layer is essential for a data lake, providing a robust framework for managing data access, quality, and usability. This layer is typically implemented through a combination of configurations, third-party tools, and specialized teams.

Governance establishes and enforces rules, policies, and procedures for data access, quality, and usability, ensuring information consistency and responsible use. Tools like Apache Atlas or Collibra can add this governance layer, enabling robust policy management and metadata tagging.

Security protocols safeguard against unauthorized data access and ensure compliance with data protection regulations. Solutions such as Varonis or McAfee Total Protection for Data Loss Prevention can be integrated to fortify this aspect of your data lake.

Monitoring and ELT (Extract, Load, Transform) processes handle the oversight and flow of data from its raw form into more usable formats. Tools like Talend or Apache NiFi specialize in streamlining these processes while maintaining performance standards.

Stewardship involves active data management and oversight, often performed by specialized teams or designated data owners. Platforms like Alation or Waterline Data assist in this role by tracking who adds, modifies, or deletes data and managing the metadata.

Access control lists (ACLs) in Azure Data Lake Storage Gen2 provide more fine-grained permissions, making them a better choice for production environments.

Data integration and AI are key components of a Power BI data lake. Power BI dataflows are used to ingest key analytics data from the Wide World Importers operational database into the organization’s Azure Data Lake Storage account.

Data can be formatted and prepared using Azure Databricks and stored in a new CDM folder in Azure Data Lake. This makes it easier to access and use the data for machine learning and other analytics tasks.

Azure Machine Learning can read data from the CDM folder to train and publish a machine learning model that can be accessed from Power BI or other applications to make real-time predictions.

Power BI and Azure data services are taking the first steps to enable data exchange and interoperability through the Common Data Model and Azure Data Lake Storage.

The Common Data Model (CDM) is a key component in this integration, allowing data to be shared between Power BI and Azure data services. This is a game-changer for organizations looking to break down data silos and unlock new insights.

One example of this integration in action is the Power BI, Azure Data and AI integration sample. In this tutorial, Power BI dataflows are used to ingest key analytics data from the Wide World Importers operational database into the organization’s Azure Data Lake Storage account.

Azure Databricks is then used to format and prepare the data, storing it in a new CDM folder in Azure Data Lake. This formatted data is then read by Azure Machine Learning to train and publish a machine learning model.

The machine learning model can be accessed from Power BI, or other applications, to make real-time predictions. In parallel, the data from the CDM folder is loaded into staging tables in an Azure SQL Data Warehouse by Azure Data Factory, where it’s transformed into a dimensional model.

This integration is made possible by Azure Data Lake Storage, which allows services to interoperate over a single repository. The diagram below illustrates the samples scenario showing how services can interoperate over Azure Data Lake with CDM folders.

The Power BI community is a great resource for learning more about this integration and how to implement it in your own organization.

Analytical sandboxes are isolated environments for data exploration, where you can experiment with data without affecting the main data flow.

In these sandboxes, you can ingest both raw and processed data, which is useful for different types of analysis. Raw data is great for exploratory activities where the original context is critical, while processed data is used for more refined analytics and machine learning models.

Data discovery is the initial step in these sandboxes, where you explore the data to understand its structure, quality, and potential value. This often involves descriptive statistics and data visualization.

Machine learning algorithms can be applied in these sandboxes to create predictive or classification models, using libraries like TensorFlow, PyTorch, or Scikit-learn.

Exploratory data analysis (EDA) is used to analyze the data and understand the variables' relationships, patterns, or anomalies without making any assumptions. Statistical graphics, plots, and information tables are employed during EDA.

Tools like Jupyter Notebooks, RStudio, or specialized software like Dataiku or Knime are often used within these sandboxes to create workflows, script, and run analyses.

IoT analytics is a game-changer for industries that deal with vast amounts of data from devices like sensors, cameras, and machinery.

Data lakes can handle this volume and variety, allowing industries to make more informed decisions. General Electric uses its industrial data lake to handle real-time IoT device data.

This enables optimized manufacturing processes and predictive maintenance in the aviation and healthcare sectors. GE's subsidiary, GE Healthcare, adopted a new data lakehouse architecture using AWS services with the Amazon S3 data lake to store raw enterprise and event data.

By leveraging IoT analytics, industries can gain valuable insights and improve their operations.

Real-Time Analytics is a game-changer in finance and eCommerce, where data is analyzed as soon as it becomes available.

Stock prices fluctuate in seconds, and real-time recommender systems can boost sales. Data lakes excel in real-time analytics because they can scale to accommodate high volumes of incoming data.

Uber uses data lakes to enable real-time analytics that support route optimization, pricing strategies, and fraud detection. This real-time processing allows Uber to make immediate data-driven decisions.

Data sources are the starting point for any data lake architecture, and understanding the type of data you're working with is crucial. Structured data sources, like SQL databases, are organized and clearly defined, while semi-structured data sources, such as HTML and XML files, require further processing to become fully structured.

Unstructured data sources, including sensor data and social media content, don't have a predefined structure. Data ingestion is the process of importing data into the data lake from various sources. It can be done in batch mode, where large chunks of data are transferred at scheduled intervals, or in real-time mode, where data is immediately brought into the lake as it's generated.

Batch ingestion tools like Apache NiFi and Flume are often used for scheduled data importation, while real-time ingestion tools like Apache Kafka and AWS Kinesis are used for time-sensitive applications.

Data sources are the starting point for any data lake architecture. They can be broadly classified into three categories: structured, semi-structured, and unstructured data sources.

Structured data sources are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Common structured data sources include SQL databases like MySQL, Oracle, and Microsoft SQL Server.

Semi-structured data sources have some level of organization but don't fit neatly into tabular structures. Examples include HTML, XML, and JSON files, which require further processing to become fully structured.

Unstructured data sources include a diverse range of data types that do not have a predefined structure. Examples of unstructured data can range from sensor data in IoT applications, videos and audio streams, images, and social media content like tweets or Facebook posts.

Understanding the data source type is crucial as it impacts subsequent steps in the data lake pipeline, including data ingestion methods and processing requirements.

Data storage and processing is a crucial step in the data ingestion process. The data storage and processing layer is where ingested data resides and undergoes transformations to make it more accessible and valuable for analysis.

Raw data is initially stored in a raw data store, which is a repository where data is staged before any form of cleansing or transformation. This zone utilizes storage solutions like Hadoop HDFS, Amazon S3, or Azure Blob Storage.

Data cleansing is a critical process that involves removing or correcting inaccurate records, discrepancies, or inconsistencies in the data. Data enrichment adds value to the original data set by incorporating additional information or context.

Normalization modifies the data into a common format, ensuring consistency. Structuring often involves breaking down unstructured or semi-structured data into a structured form suitable for analysis.

The data becomes trusted data after these transformations, which is more reliable, clean, and suitable for various analytics and machine learning models.