Build a delta lake to support ad hoc queries in online leisure and travel booking

Azure Event Hubs
Azure Data Lake Storage
Azure Databricks
Azure Synapse Analytics

This architecture provides an example delta lake for travel booking, where large amounts of raw documents are generated at a high frequency.

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Diagram of Delta Lake architecture.

Download a Visio file of this architecture.

Leisure and travel booking scenarios can generate large amounts of raw documents at a high frequency. However, you may not need to index the entire contents of these documents. For example, users may need to search by a known transaction ID, or by a customer name on a certain date, to retrieve a set of documents that is interesting to them.


The concept behind this architecture consists in decoupling the metadata useful for searching from bare data:

  • Only metadata gets indexed within a queryable service (such as Spark), while the actual data is stored in a data lake.
  • Raw documents in a data lake are linked to indexed metadata by their path.
  • When querying for documents, the service searches the documents' metadata, and in turn the actual documents will be retrieved from the data lake by their path.

This solution dramatically lowers costs and increases performance, as metadata comprises a fraction of the entire data estate (for instance, petabytes of raw documents can be described by tens of gigabytes of concise metadata).

In addition, managing the blending of historical depth and real-time requirements into a uniform, easy-to-maintain, high-performance system is a typical challenge of this type of scenario. The Delta Lake architecture answers this challenge.


Azure App Service is a platform as a service (PaaS) for building and hosting apps in managed virtual machines. App Service manages the underlying compute infrastructure on which your apps run, and it provides monitoring of resource usage quotas and app metrics, logging of diagnostic information, and alerts based on metrics.

Azure Data Factory is Azure's cloud extract, transform, and load (ETL) service for scale-out serverless data integration and data transformation. It offers a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. You can also lift and shift existing SQL Server Integration Services (SSIS) packages to Azure and run them with full compatibility in Azure Data Factory.

Azure Data Lake Storage Gen2 is a set of capabilities dedicated to big data analytics, built on Azure Blob Storage. Data Lake Storage Gen2 converges the capabilities of Azure Data Lake Storage Gen1 with Azure Blob Storage. For example, Data Lake Storage Gen2 provides file system semantics, file-level security, and scale. Because these capabilities are built on Blob Storage, you also get low-cost, tiered storage, with high availability/disaster recovery capabilities.

Azure Event Hubs is a fully managed, real-time data ingestion service that's simple, trusted, and scalable. Stream millions of events per second from any source to build dynamic data pipelines and immediately respond to business challenges.

Azure Databricks is an Apache Spark–based data analytics platform optimized for Microsoft Azure Cloud Services. Azure Databricks offers three environments for developing data intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.


As an alternative to only indexing metadata, you could index all raw data in a service that offers query capabilities, such as Azure Databricks, Azure Synapse Analytics, Azure Cognitive Search, or Azure Data Explorer. This approach is more immediate, but pay attention to the combined effect of data size, performance requirements, and update frequency, especially from a cost perspective.

Contrary to using a delta lake, using a Lambda architecture keeps real-time data in a different repository than historical data, and your client runs the logic to make heterogeneous queries transparent to the user. The advantage of this solution is the larger set of services that you can use (such as Azure Stream Analytics and Azure SQL Database), but the architecture becomes more complex and the code base more expensive to maintain.

Spark is distributed with Azure Databricks, Azure Synapse Analytics, and Azure HDInsight. Hence, this architecture could be implemented with any of these Azure data services, preferably with a recent Spark version supporting Delta Lake 0.8 or 1.0.

Scenario details

The visibility of raw data in leisure and travel booking scenarios is important to multiple actors. Technical support teams oversee real-time diagnostics to continuously monitor transaction processing and rapidly react to undesired issues. Data engineers oversee exporting data for stakeholder review and to feed analytics in real time. Customer support teams require historical and recent data to handle customer inquiries and complaints. Finally, legal teams ensure compliance duties are respected and legal actions performed. These types of requirements are typical in marketplaces that aggregate external providers and manage user purchases. For example, leisure and travel booking systems disintermediate users and services providers for searching services, aggregating meaningful offers from providers, and managing user reservations.

Diagram of a marketplace with service providers and B2B and B2C users.

Potential use cases

This architecture is ideal for the travel and hospitality industries. It's applicable to the following scenarios:

  • Quickly retrieving either real-time (for example, for diagnostics) or historical (for compliance) raw documents in their original format.
  • Managing petabytes of data.
  • Guaranteeing seconds-range performance for real-time diagnostics.
  • Achieving a unified approach to real-time diagnostics, historical queries, and feeding analytics.
  • Feeding downstream real-time analytics.
  • Controlling costs.
  • Insourcing data as raw documents (for instance, as json, xml, or csv files).
  • When a fraction of data is sufficient to describe queries.
  • When users want to retrieve full raw documents.
  • When the total data size would require scaling the system above your target price.

This architecture might not be suitable when:

  • Data is insourced as recordsets.
  • Users are required to run analytics.
  • Users are willing to use their own packaged BI tool.
  • The size of data is not a challenge from a cost perspective.

Raw documents are not necessarily required.


These considerations implement the pillars of the Azure Well-Architected Framework, which is a set of guiding tenets that can be used to improve the quality of a workload. For more information, see Microsoft Azure Well-Architected Framework.

Performance efficiency

Performance efficiency is the ability of your workload to scale to meet the demands placed on it by users in an efficient manner. For more information, see Performance efficiency pillar overview.

Users will perform a double hop to access data. They'll query metadata first, and then retrieve the desired set of documents. It might be difficult to reuse existing or packaged client assets.

Azure Data Lake Storage Gen2 provides three access tiers: hot, cool, and archive. In scenarios where documents are occasionally retrieved, the cool performance tier should guarantee similar performance to the hot performance tier but with the advantage of lower costs. In scenarios where the probability of retrieval is higher with newer data, consider blending the cool and hot tiers. Using archive tier storage could also provide an alternative to hard deletion, as well as reduce the size of data by keeping only meaningful information or more aggregated data.

The data lake will potentially manage petabytes of data, so data retention policies generally apply. Data governance solutions should be employed to manage data lifecycle, such as when to move old data between hot and cool storage tiers, when to delete or archive old data, and when to aggregate information into a downstream analytics solution.

Consider how this approach might work with downstream analytics scenarios. Although this example workload is not meant for analytics, it is appropriate for feeding downstream real-time analytics, while batch scenarios could be fed from the data lake instead.


Azure Event Hubs is highly versatile when it comes to decoupling a transactional system that generates raw documents from a diagnostics and compliance system; is easy to implement in already-established architectures; and, ultimately, is easy to use. However, the transactional system might already use the streaming pattern to process incoming documents. In that case, you would likely need to integrate logic for managing diagnostics and compliance into the streaming application as a substream.


For deploying the used services in this example workload automatically, it's best to use continuous integration and continuous deployment (CI/CD) processes. Consider using a solution such as Azure DevOps or GitHub Actions.

Cost optimization

Cost optimization is about looking at ways to reduce unnecessary expenses and improve operational efficiencies. For more information, see Overview of the cost optimization pillar.

In general, use the Azure pricing calculator to estimate costs. See the cost section in Microsoft Azure Well-Architected Framework to learn about other considerations.

Deploy this scenario

In the following example architecture, we assume that one or more Azure Event Hubs namespaces will contain structured raw documents (such as json or xml files). However, the actual type and format of documents and source services, and their type of integration, is highly dependent on the specific scenario and architecture.


With Spark Structured Streaming, raw data is pulled, decompressed, parsed, and translated to tabular data in a streaming DataFrame.

The following PySpark code snippet is used to load a streaming DataFrame from Event Hubs:

# Code tested in Databricks with Delta Lake 1.0
eh_connstr = <your_conn_str>
eh_consumergroup = <your_consumer_group>
ehConf = {}
ehConf['eventhubs.connectionString'] =
ehConf['eventhubs.consumerGroup'] = eh_consumergroup

streaming_df = spark \
  .readStream \
  .format("eventhubs") \
  .options(**ehConf) \

The following code snippet is used to process the streaming DataFrame. It first decompresses the Event Hubs message if necessary, and then parses its json structure to a tabular format. This code is an example and should be adapted to your specific scenario:

# Code tested in Databricks with Delta Lake 1.0

# defines an UDF to unzip the Event Hubs Body field, assuming it 
is gzipped

import zlib
def DecompressFunction(data):
  decoded_data = zlib.decompress(bytes(data), 15+32)
  return decoded_data.decode()

Decompress = udf(lambda body: DecompressFunction(body), 
decoded_body_df = streaming_df.withColumn("DecodedBody", 

# Parse json message from Event Hubs body, assuming the raw 
document is stored in the data field, and the others fields hold 
some metadata about it

schema = StructType([ \
    StructField("transactionId", LongType(),True), \
    StructField("timestamp",TimestampType(),True), \
    StructField("providerName", StringType(),True), \
    StructField("document", StringType(),True), \
    StructField("documentType", StringType(),True)

parsed_body_df = decoded_body_df.withColumn("jsonBody", 
from_json(col("DecodedBody"), schema)).select("jsonBody")

Actual data processing consists of two steps. The first is to extract metadata to assist searching the raw documents after processing. Actual metadata depends on the use case, but generalizable examples would be relevant dates and identifiers, document types, source service, and any type of category:

# Code tested in Databricks with Delta Lake 1.0

df = parsed_body_df \
parsed_body_df.jsonBody.transactionId) \
    .withColumn("timestamp", parsed_body_df.jsonBody.timestamp) \
parsed_body_df.jsonBody.providerName) \

The second processing step is to generate a path to Azure Data Lake Storage Gen2, where you'll store raw documents:

# Code tested in Databricks with Delta Lake 1.0

# A function to generate a path
def GetPathFunction(timeStamp, transactionId, providerName, 
Suffix='', Extension=".gz"):
  yy = timeStamp.year
  mm = timeStamp.month
  dd =
  hh = timeStamp.hour
  mn = timeStamp.minute
  Suffix = f"{Suffix}_" if Suffix != '' else ''
  Name = f"{Suffix}{providerName}{Extension}"
  path = f"/{yy}/{mm}/{dd}/{hh}/{mn}/{transactionId}/{Name}"
  return path

GetPath = udf(lambda timestamp, transactionId, providerName, 
suffix, extension: GetPathFunction(timestamp, transactionId, 
providerName, suffix, extension), StringType())

df = df.withColumn("path", GetPath(col("timestamp"), 
col("transactionId"), col("providerName"), col('documentType')))

Metadata ingestion in a delta lake

Metadata is written to a delta table that enables real-time query capabilities. Writes are streamed in a buffer, and queries to the table can merge results from the buffer with those from the historical portion of the table.

The following code snippet shows how to define a delta table in the metastore and partition it by date:

# Code tested in Databricks with Delta Lake 1.0

DeltaTable.create(spark) \
   .tableName("metadata") \
   .addColumn("transactionId", LongType()) \
   .addColumn("date", TimestampType()) \
   .addColumn("providerName", StringType()) \
   .addColumn("documentType", StringType()) \
   .addColumn("path", StringType()) \
   .partitionedBy("date") \

Note that the transactionId field is numeric. Typical messages passing distributed systems might use GUIDs to uniquely identify transactions instead. However, numeric data types enable greater query performance in most data platforms.

Assigning a unique transaction identifier might be challenging given the distributed nature of cloud data platforms (such as Spark). A useful approach is to base such a transaction identifier on a partition identifier (like the Event Hubs partition number) and a within-partition incremental number. An example of this approach is monotonically_increasing_id() in Azure Databricks.

The following code snippet shows how to append the stream with metadata of raw documents to the delta table:

# Code tested in Databricks with Delta Lake 1.0

df.withColumn("date", col("timeStamp").cast(DateType())) \
    .select("transactionId", "date", "providerName", 
"documentType", "path") \
    .writeStream.format("delta") \
    .outputMode("append") \
"/delta/metadata/_checkpoints/metadata_checkpoint") \

Note that partitioning is managed while writing the stream according to the table schema.

Data ingestion in a data lake

Actual raw documents are written to an appropriate storage performance tier in Azure Data Lake Gen2.

The following code snippet shows a simple function to upload a file to Azure Data Lake Store Gen2; using a foreach method in the DataStreamWriter class allows you to upload the file hosted in each record of the streaming DataFrame:

# Code tested in Databricks with Delta Lake 1.0

from import DataLakeServiceClient

def upload_data(storage_account_name, storage_account_key, 
file_system_name, file_path, data):

  service_client = 
format("https", storage_account_name), 

  file_system_client = 
  file_client = 
e, file_path)
  if not file_client.exists:

  file_client.upload_data(data, overwrite=True)
# Process a row to upload data to ADLS
def Row2ADLS(row):
  upload_data(adls_name, adls_key, adls_container, row['path'], 



The client can be a custom web application that uses metadata to retrieve document paths from the delta table with standard SQL statements and, in turn, the actual document from the data lake with standard Azure Data Lake Storage Gen2 APIs.

The following code snippet, for example, shows how to retrieve the paths of all the documents in a certain transaction:

select * from metadata where transactionId = '123456'

Next steps

See the related architectural guidance:

See these related architectures: