Delta Lake is an open-source storage layer that brings reliability to data lakes. With Delta Lake, you can build robust, high-performance data pipelines and get more accurate insights leveraging your data lake. More so, Azure Data Lake Storage Gen2 is a highly scalable and cost-effective data lake solution for big data analytics. This post will focus on reading from and writing to a Delta Lake in the context of DP-203 Data Engineering on Microsoft Azure.
Reading from a Delta Lake
When reading data from a Delta Lake, you can use the DELTA format in Apache Spark, which is supported in Azure Databricks. The syntax is fairly simple. You simply specify the format as “delta” and provide the path to the Delta Lake. Here is an example:
df = spark.read.format("delta").load("/mnt/delta/events")
In this example, spark is a SparkSession, which is an entry point to any functionality in Spark. The read function tells Spark that we are going to read data, format denotes the format of the data, and load specifies the path to the data.
With Delta Lake you can perform advanced analytics like data versioning, time travel queries, and audit history.
Writing to a Delta Lake
Writing data to a Delta Lake is as straightforward as reading from it. Just like reading, you specify the format as “delta” and provide the path to the Delta Lake. However, you would use the write function instead of read, and save instead of load. Here is an example:
df.write.format("delta").save("/mnt/delta/events")
This code snipe will write the DataFrame df into a Delta Lake located at “/mnt/delta/events”.
You can also specify different write modes – ‘append’, ‘overwrite’, ‘ignore’, or ‘errorifexists’. For example, to append data to an existing dataset, you would do this:
df.write.format("delta").mode("append").save("/mnt/delta/events")
And to overwrite an existing dataset:
df.write.format("delta").mode("overwrite").save("/mnt/delta/events")
You can use Delta Lake to ensure data reliability and integrity, thanks to its ACID transactions, scalable metadata handling, and unified batch and streaming source and sink.
Conclusion
Delta Lake offers a powerful way to read from and write to your data lake. Its integration with Azure makes it even more effective for big data analytics. Working with it requires a good understanding of Apache Spark and the concept of DataFrames.
By understanding how to read from and write to a Delta Lake, you are one step closer to acing the DP-203 Data Engineering on Microsoft Azure exam. So keep on studying and practicing, and remember, “Practice is the key to mastering any subject.”
Practice Test
True or False: Delta Lake is a transactional storage layer that sits on top of your existing data lake.
– True
– False
Answer: True
Explanation: Delta Lake is a transactional storage layer that uses Apache Spark APIs to bring reliability and performance to your existing data lake.
Which of the following is the language you use to read from and write to a Delta Lake?
– SQL
– Python
– Java
– All of the above
Answer: All of the above
Explanation: Programmatic interfaces for Delta Lake are available in SQL, Python, Java, which making it flexible for different coding capabilities.
True or False: Delta Lake supports Update and Delete operations apart from Insert and Read.
– True
– False
Answer: True
Explanation: Delta Lake supports all standard DML operations including Insert, Read, Update and Delete.
Delta Lake can be used with:
– Azure Databricks
– Apache Spark
– Both
– None
Answer: Both
Explanation: Delta Lake can be used with both Azure Databricks and Apache Spark.
True or False: You cannot perform concurrent writes and reads to a Delta lake.
– True
– False
Answer: False
Explanation: Delta Lake supports concurrent reads and writes, thereby enabling high volume data pipelines.
Can we write data to a Delta Lake using Apache Kafka?
– Yes
– No
Answer: Yes
Explanation: Apache Kafka, or any other stream or batch data source that Apache Spark Supports, can be used to write data to a Delta Lake.
What does ACID compliance mean in context of Delta Lake?
– Allows Multiple Readers and Writers
– Ensures Data Consistency
– Permits Data Time Travel
– All of the above
Answer: All of the above
Explanation: ACID compliance, Atomicity, Consistency, Isolation, and Durability, ensures data consistency atop distributed, scalable storage systems.
How does Delta Lake handle schema evolution?
– Automatically infers and applies schema
– Supports manual schema evolution
– Does not support schema evolution
– None of the above
Answer: Supports manual schema evolution
Explanation: Delta Lake has the ability to handle schema evolution through user controlled and explicit schema evolution.
True or False: You can convert a Parquet data into Delta Lake format.
– True
– False
Answer: True
Explanation: You can convert an existing Parquet data into Delta Lake format with a single command without the need for rewriting or moving data.
What is the necessity of Delta Lake in Data Lake architecture?
– To provide ACID transaction
– To enable real-time data processing
– To handle huge volume of data
– All of the above
Answer: All of the above
Explanation: Delta Lake is integral in a Data Lake architecture as it provides ACID transactions, scalable metadata handling and unifies streaming and batch data processing while working with huge volume of data.
Interview Questions
What command is used to read data from a Delta Lake in Microsoft Azure?
We use the command
spark.read.format("delta").load().show() .
How do you perform write operations to a Delta Lake on Microsoft Azure?
You can use the
DataFrameWriterAPI to write data to a Delta table. The general command is
dataframe.write.format("delta").save() .
How do you overwrite data in Delta Lake on Azure?
You can use the command:
dataframe.write.format('delta').mode('overwrite').save() .
What is Delta Lake in Microsoft Azure?
Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to Apache Spark and big data workloads in Azure.
What are the benefits of using Delta Lake instead of Parquet?
Unlike Parquet, Delta Lake provides ACID transactions, scalable metadata handling and unifies streaming and batch data processing, allowing us to read and write structured data using Spark.SQL, DataFrames, and Datasets.
How to create a delta table from an existing parquet table on Azure?
You can use the command
spark.sql("CONVERT TO DELTA parquet.``") .
How to read a specific version of a Delta Lake table in Azure?
You can use
spark.read.format("delta").option("versionAsOf", version).load() .
Why is the time travel feature important in Delta Lake?
The time travel feature of Delta Lake lets you access and revert to earlier versions of the data. This is useful for conducting data audits, reproducing experiments, and rolling back any disastrous updates.
How to delete data from Delta Table in Azure?
You can use the
DELETE FROMSQL command available in Delta Lake.
How to perform an UPSERT operation in Delta Lake in Azure?
You can use the
MERGE INTOSQL command available in Delta Lake.
How to optimize the performance of accessing data in Delta Lake on Azure?
Use
OPTIMIZEcommand followed by
ZORDER BYto rearrange the data in the Delta Lake to provide efficient access to specific columns.
How to ensure the consistency of data while writing to Delta Lake on Azure?
By using ACID (Atomicity, Consistency, Isolation, Durability) transactions, Delta Lake ensures that data is written consistently to the lake even in the event of multiple concurrent write operations.
Can we run SQL directly on Files in Azure?
No. You need to TEMPORARY VIEW or CREATE TABLE to run SQL on those files.
Can Delta Lake handle streaming and batch data?
Yes, Delta Lake unifies both streaming and batch data processing by storing data in a Delta table, which can be read by both streaming and batch jobs in parallel.
How to update the metadata of Delta Lake in Azure?
You can use the
ALTER TABLESQL command to add, change, or drop the metadata of the Delta table. For example,
ALTER TABLE delta.`` ADD COLUMNS (col String) .
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