- Article
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This article has been adapted for more clarity from its original counterpart here. This article helps you quickly explore the main features of Delta Lake. The article provides code snippets that show how to read from and write to Delta Lake tables from interactive, batch, and streaming queries. The code snippets are also available in a set of notebooks PySpark here, Scala here, and C# here
Here's what we will cover:
- Create a table
- Read data
- Update table data
- Overwrite table data
- Conditional update without overwrite
- Read older versions of data using Time Travel
- Write a stream of data to a table
- Read a stream of changes from a table
- SQL Support
Configuration
Make sure you modify the below as appropriate for your environment.
import randomsession_id = random.randint(0,1000000)delta_table_path = "/delta/delta-table-{0}".format(session_id)delta_table_pathvar sessionId = (new Random()).Next(10000000);var deltaTablePath = $"/delta/delta-table-{sessionId}";deltaTablePathval sessionId = scala.util.Random.nextInt(1000000)val deltaTablePath = s"/delta/delta-table-$sessionId";Results in:
'/delta/delta-table-335323'
Create a table
To create a Delta Lake table, write a DataFrame out a DataFrame in the delta format. You can change the format from Parquet, CSV, JSON, and so on, to delta.
The code that follows shows you how to create a new Delta Lake table using the schema inferred from your DataFrame.
data = spark.range(0,5)data.show()data.write.format("delta").save(delta_table_path)var data = spark.Range(0,5);data.Show();data.Write().Format("delta").Save(deltaTablePath);val data = spark.range(0, 5)data.showdata.write.format("delta").save(deltaTablePath)Results in:
| ID |
|---|
| 0 |
| 1 |
| 2 |
| 3 |
| 4 |
Read data
You read data in your Delta Lake table by specifying the path to the files and the delta format.
df = spark.read.format("delta").load(delta_table_path)df.show()var df = spark.Read().Format("delta").Load(deltaTablePath);df.Show()val df = spark.read.format("delta").load(deltaTablePath)df.show()Results in:
| ID |
|---|
| 1 |
| 3 |
| 4 |
| 0 |
| 2 |
The order of the results is different from above as there was no order explicitly specified before outputting the results.
Update table data
Delta Lake supports several operations to modify tables using standard DataFrame APIs. These operations are one of the enhancements that delta format adds. The following example runs a batch job to overwrite the data in the table.
data = spark.range(5,10)data.write.format("delta").mode("overwrite").save(delta_table_path)df.show()var data = spark.Range(5,10);data.Write().Format("delta").Mode("overwrite").Save(deltaTablePath);df.Show();val data = spark.range(5, 10)data.write.format("delta").mode("overwrite").save(deltaTablePath)df.show()Results in:
| ID |
|---|
| 7 |
| 8 |
| 5 |
| 9 |
| 6 |
Here you can see that all five records have been updated to hold new values.
Save as catalog tables
Delta Lake can write to managed or external catalog tables.
data.write.format("delta").saveAsTable("ManagedDeltaTable")spark.sql("CREATE TABLE ExternalDeltaTable USING DELTA LOCATION '{0}'".format(delta_table_path))spark.sql("SHOW TABLES").show()data.Write().Format("delta").SaveAsTable("ManagedDeltaTable");spark.Sql($"CREATE TABLE ExternalDeltaTable USING DELTA LOCATION '{deltaTablePath}'");spark.Sql("SHOW TABLES").Show();data.write.format("delta").saveAsTable("ManagedDeltaTable")spark.sql(s"CREATE TABLE ExternalDeltaTable USING DELTA LOCATION '$deltaTablePath'")spark.sql("SHOW TABLES").showResults in:
| database | tableName | isTemporary |
|---|---|---|
| default | externaldeltatable | false |
| default | manageddeltatable | false |
With this code, you created a new table in the catalog from an existing dataframe, referred to as a managed table. Then you defined a new external table in the catalog that uses an existing location, referred to as an external table. In the output you can see both tables, no matter how they were created, are listed in the catalog.
Now you can look at the extended properties of both of these tables
spark.sql("DESCRIBE EXTENDED ManagedDeltaTable").show(truncate=False)spark.Sql("DESCRIBE EXTENDED ManagedDeltaTable").Show(truncate: 0);spark.sql("DESCRIBE EXTENDED ManagedDeltaTable").show(truncate=false)Results in:
| col_name | data_type | comment |
|---|---|---|
| id | bigint | null |
| Detailed Table Information | ||
| Database | default | |
| Table | manageddeltatable | |
| Owner | trusted-service-user | |
| Created Time | Sat Apr 25 00:35:34 UTC 2020 | |
| Last Access | Thu Jan 01 00:00:00 UTC 1970 | |
| Created By | Spark 2.4.4.2.6.99.201-11401300 | |
| Type | MANAGED | |
| Provider | delta | |
| Table Properties | [transient_lastDdlTime=1587774934] | |
| Statistics | 2407 bytes | |
| Location | abfss://data@<data lake>.dfs.core.windows.net/synapse/workspaces/<workspace name>/warehouse/manageddeltatable | |
| Serde Library | org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | |
| InputFormat | org.apache.hadoop.mapred.SequenceFileInputFormat | |
| OutputFormat | org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat | |
| Storage Properties | [serialization.format=1] |
spark.sql("DESCRIBE EXTENDED ExternalDeltaTable").show(truncate=False)spark.Sql("DESCRIBE EXTENDED ExternalDeltaTable").Show(truncate: 0);spark.sql("DESCRIBE EXTENDED ExternalDeltaTable").show(truncate=false)Results in:
| col_name | data_type | comment |
|---|---|---|
| id | bigint | null |
| Detailed Table Information | ||
| Database | default | |
| Table | externaldeltatable | |
| Owner | trusted-service-user | |
| Created Time | Sat Apr 25 00:35:38 UTC 2020 | |
| Last Access | Thu Jan 01 00:00:00 UTC 1970 | |
| Created By | Spark 2.4.4.2.6.99.201-11401300 | |
| Type | EXTERNAL | |
| Provider | DELTA | |
| Table Properties | [transient_lastDdlTime=1587774938] | |
| Location | abfss://data@<data lake>.dfs.core.windows.net/delta/delta-table-587152 | |
| Serde Library | org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | |
| InputFormat | org.apache.hadoop.mapred.SequenceFileInputFormat | |
| OutputFormat | org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat | |
| Storage Properties | [serialization.format=1] |
Conditional update without overwrite
Delta Lake provides programmatic APIs to conditional update, delete, and merge (this command is commonly referred to as an upsert) data into tables.
from delta.tables import *from pyspark.sql.functions import *delta_table = DeltaTable.forPath(spark, delta_table_path)delta_table.update( condition = expr("id % 2 == 0"), set = { "id": expr("id + 100") })delta_table.toDF().show()using Microsoft.Spark.Extensions.Delta;using Microsoft.Spark.Extensions.Delta.Tables;using Microsoft.Spark.Sql;using static Microsoft.Spark.Sql.Functions;var deltaTable = DeltaTable.ForPath(deltaTablePath);deltaTable.Update( condition: Expr("id % 2 == 0"), set: new Dictionary<string, Column>(){{ "id", Expr("id + 100") }});deltaTable.ToDF().Show();import io.delta.tables._import org.apache.spark.sql.functions._val deltaTable = DeltaTable.forPath(deltaTablePath)// Update every even value by adding 100 to itdeltaTable.update( condition = expr("id % 2 == 0"), set = Map("id" -> expr("id + 100")))deltaTable.toDF.showResults in:
| ID |
|---|
| 106 |
| 108 |
| 5 |
| 7 |
| 9 |
Here you just added 100 to every even ID.
delta_table.delete("id % 2 == 0")delta_table.toDF().show()deltaTable.Delete(condition: Expr("id % 2 == 0"));deltaTable.ToDF().Show();deltaTable.delete(condition = expr("id % 2 == 0"))deltaTable.toDF.showResults in:
| ID |
|---|
| 5 |
| 7 |
| 9 |
Notice that every even row has been deleted.
new_data = spark.range(0,20).alias("newData")delta_table.alias("oldData")\ .merge(new_data.alias("newData"), "oldData.id = newData.id")\ .whenMatchedUpdate(set = { "id": lit("-1")})\ .whenNotMatchedInsert(values = { "id": col("newData.id") })\ .execute()delta_table.toDF().show(100)var newData = spark.Range(20).As("newData");deltaTable .As("oldData") .Merge(newData, "oldData.id = newData.id") .WhenMatched() .Update(new Dictionary<string, Column>() {{"id", Lit("-1")}}) .WhenNotMatched() .Insert(new Dictionary<string, Column>() {{"id", Col("newData.id")}}) .Execute();deltaTable.ToDF().Show(100);val newData = spark.range(0, 20).toDFdeltaTable.as("oldData"). merge( newData.as("newData"), "oldData.id = newData.id"). whenMatched. update(Map("id" -> lit(-1))). whenNotMatched. insert(Map("id" -> col("newData.id"))). execute()deltaTable.toDF.show()Results in:
| ID |
|---|
| 18 |
| 15 |
| 19 |
| 2 |
| 1 |
| 6 |
| 8 |
| 3 |
| -1 |
| 10 |
| 13 |
| 0 |
| 16 |
| 4 |
| -1 |
| 12 |
| 11 |
| 14 |
| -1 |
| 17 |
Here you have a combination of the existing data. The existing data has been assigned the value -1 in the update(WhenMatched) code path. The new data that was created at the top of the snippet and was added via the insert code path (WhenNotMatched), was also added.
History
Delta Lake's has the ability to allow looking into history of a table. That is, the changes that were made to the underlying Delta Table. The cell below shows how simple it's to inspect the history.
delta_table.history().show(20, 1000, False)deltaTable.History().Show(20, 1000, false);deltaTable.history.show(false)Results in:
| version | timestamp | userId | userName | operation | operationParameters | job | notebook | clusterId | readVersion | isolationLevel | isBlindAppend |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 2020-04-25 00:36:27 | null | null | MERGE | [predicate -> (oldData.ID = newData.ID)] | null | null | null | 3 | null | false |
| 3 | 2020-04-25 00:36:08 | null | null | DELETE | [predicate -> ["((ID % CAST(2 AS BIGINT)) = CAST(0 AS BIGINT))"]] | null | null | null | 2 | null | false |
| 2 | 2020-04-25 00:35:51 | null | null | UPDATE | [predicate -> ((ID#744L % cast(2 as bigint)) = cast(0 as bigint))] | null | null | null | 1 | null | false |
| 1 | 2020-04-25 00:35:05 | null | null | WRITE | [mode -> Overwrite, partitionBy -> []] | null | null | null | 0 | null | false |
| 0 | 2020-04-25 00:34:34 | null | null | WRITE | [mode -> ErrorIfExists, partitionBy -> []] | null | null | null | null | null | true |
Here you can see all of the modifications made over the above code snippets.
Read older versions of data using Time Travel
It's possible to query previous snapshots of your Delta Lake table by using a feature called Time Travel. If you want to access the data that you overwrote, you can query a snapshot of the table before you overwrote the first set of data using the versionAsOf option.
Once you run the cell below, you should see the first set of data from before you overwrote it. Time Travel is a powerful feature that takes advantage of the power of the Delta Lake transaction log to access data that is no longer in the table. Removing the version 0 option (or specifying version 1) would let you see the newer data again. For more information, see Query an older snapshot of a table.
df = spark.read.format("delta").option("versionAsOf", 0).load(delta_table_path)df.show()var df = spark.Read().Format("delta").Option("versionAsOf", 0).Load(deltaTablePath);df.Show();val df = spark.read.format("delta").option("versionAsOf", 0).load(deltaTablePath)df.show()Results in:
| ID |
|---|
| 0 |
| 1 |
| 4 |
| 3 |
| 2 |
Here you can see you've gone back to the earliest version of the data.
Write a stream of data to a table
You can also write to a Delta Lake table using Spark's Structured Streaming. The Delta Lake transaction log guarantees exactly once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table.
For more information about Delta Lake integration with Structured Streaming, see Table Streaming Reads and Writes.
In the cells below, here's what we are doing:
- Cell 30 Show the newly appended data
- Cell 31 Inspect history
- Cell 32 Stop the structured streaming job
- Cell 33 Inspect history <--You'll notice appends have stopped
First you're going to set up a simple Spark Streaming job to generate a sequence and make the job write to your Delta Table.
streaming_df = spark.readStream.format("rate").load()stream = streaming_df\ .selectExpr("value as id")\ .writeStream\ .format("delta")\ .option("checkpointLocation", "/tmp/checkpoint-{0}".format(session_id))\ .start(delta_table_path)var streamingDf = spark.ReadStream().Format("rate").Load();var stream = streamingDf.SelectExpr("value as id").WriteStream().Format("delta").Option("checkpointLocation", $"/tmp/checkpoint-{sessionId}").Start(deltaTablePath);val streamingDf = spark.readStream.format("rate").load()val stream = streamingDf.select($"value" as "id").writeStream.format("delta").option("checkpointLocation", s"/tmp/checkpoint-$sessionId").start(deltaTablePath)Read a stream of changes from a table
While the stream is writing to the Delta Lake table, you can also read from that table as a streaming source. For example, you can start another streaming query that prints all the changes made to the Delta Lake table.
delta_table.toDF().sort(col("id").desc()).show(100)deltaTable.ToDF().Sort(Col("id").Desc()).Show(100);deltaTable.toDF.sort($"id".desc).showResults in:
| ID |
|---|
| 19 |
| 18 |
| 17 |
| 16 |
| 15 |
| 14 |
| 13 |
| 12 |
| 11 |
| 10 |
| 8 |
| 6 |
| 4 |
| 3 |
| 2 |
| 1 |
| 0 |
| -1 |
| -1 |
| -1 |
delta_table.history().drop("userId", "userName", "job", "notebook", "clusterId", "isolationLevel", "isBlindAppend").show(20, 1000, False)deltaTable.History().Drop("userId", "userName", "job", "notebook", "clusterId", "isolationLevel", "isBlindAppend").Show(20, 1000, false);deltaTable.history.showResults in:
| version | timestamp | operation | operationParameters | readVersion |
|---|---|---|---|---|
| 5 | 2020-04-25 00:37:09 | STREAMING UPDATE | [outputMode -> Append, queryId -> d26b4f8a-7e5a-44f2-a5fb-23a7bd02aef7, epochId -> 0] | 4 |
| 4 | 2020-04-25 00:36:27 | MERGE | [predicate -> (oldData.id = newData.id)] | 3 |
| 3 | 2020-04-25 00:36:08 | DELETE | [predicate -> ["((id % CAST(2 AS BIGINT)) = CAST(0 AS BIGINT))"]] | 2 |
| 2 | 2020-04-25 00:35:51 | UPDATE | [predicate -> ((id#744L % cast(2 as bigint)) = cast(0 as bigint))] | 1 |
| 1 | 2020-04-25 00:35:05 | WRITE | [mode -> Overwrite, partitionBy -> []] | 0 |
| 0 | 2020-04-25 00:34:34 | WRITE | [mode -> ErrorIfExists, partitionBy -> []] | null |
Here you're dropping some of the less interesting columns to simplify the viewing experience of the history view.
stream.stop()delta_table.history().drop("userId", "userName", "job", "notebook", "clusterId", "isolationLevel", "isBlindAppend").show(100, 1000, False)stream.Stop();deltaTable.History().Drop("userId", "userName", "job", "notebook", "clusterId", "isolationLevel", "isBlindAppend").Show(100, 1000, false);stream.stopdeltaTable.history.showResults in:
| version | timestamp | operation | operationParameters | readVersion |
|---|---|---|---|---|
| 5 | 2020-04-25 00:37:09 | STREAMING UPDATE | [outputMode -> Append, queryId -> d26b4f8a-7e5a-44f2-a5fb-23a7bd02aef7, epochId -> 0] | 4 |
| 4 | 2020-04-25 00:36:27 | MERGE | [predicate -> (oldData.id = newData.id)] | 3 |
| 3 | 2020-04-25 00:36:08 | DELETE | [predicate -> ["((id % CAST(2 AS BIGINT)) = CAST(0 AS BIGINT))"]] | 2 |
| 2 | 2020-04-25 00:35:51 | UPDATE | [predicate -> ((id#744L % cast(2 as bigint)) = cast(0 as bigint))] | 1 |
| 1 | 2020-04-25 00:35:05 | WRITE | [mode -> Overwrite, partitionBy -> []] | 0 |
| 0 | 2020-04-25 00:34:34 | WRITE | [mode -> ErrorIfExists, partitionBy -> []] | null |
Convert Parquet to Delta
You can do an in-place conversion from the Parquet format to Delta.
Here you're going to test if the existing table is in delta format or not.
parquet_path = "/parquet/parquet-table-{0}".format(session_id)data = spark.range(0,5)data.write.parquet(parquet_path)DeltaTable.isDeltaTable(spark, parquet_path)var parquetPath = $"/parquet/parquet-table-{sessionId}";var data = spark.Range(0,5);data.Write().Parquet(parquetPath);DeltaTable.IsDeltaTable(parquetPath)val parquetPath = s"/parquet/parquet-table-$sessionId"val data = spark.range(0,5)data.write.parquet(parquetPath)DeltaTable.isDeltaTable(parquetPath)Results in:
False
Now you're going to convert the data to delta format and verify it worked.
DeltaTable.convertToDelta(spark, "parquet.`{0}`".format(parquet_path))DeltaTable.isDeltaTable(spark, parquet_path)DeltaTable.ConvertToDelta(spark, $"parquet.`{parquetPath}`");DeltaTable.IsDeltaTable(parquetPath)DeltaTable.convertToDelta(spark, s"parquet.`$parquetPath`")DeltaTable.isDeltaTable(parquetPath)Results in:
True
SQL support
Delta supports table utility commands through SQL. You can use SQL to:
- Get a DeltaTable's history
- Vacuum a DeltaTable
- Convert a Parquet file to Delta
spark.sql("DESCRIBE HISTORY delta.`{0}`".format(delta_table_path)).show()spark.Sql($"DESCRIBE HISTORY delta.`{deltaTablePath}`").Show();spark.sql(s"DESCRIBE HISTORY delta.`$deltaTablePath`").show()Results in:
| version | timestamp | userId | userName | operation | operationParameters | job | notebook | clusterId | readVersion | isolationLevel | isBlindAppend |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 2020-04-25 00:37:09 | null | null | STREAMING UPDATE | [outputMode -> Ap... | null | null | null | 4 | null | true |
| 4 | 2020-04-25 00:36:27 | null | null | MERGE | [predicate -> (ol... | null | null | null | 3 | null | false |
| 3 | 2020-04-25 00:36:08 | null | null | DELETE | [predicate -> ["(... | null | null | null | 2 | null | false |
| 2 | 2020-04-25 00:35:51 | null | null | UPDATE | [predicate -> ((i... | null | null | null | 1 | null | false |
| 1 | 2020-04-25 00:35:05 | null | null | WRITE | [mode -> Overwrit... | null | null | null | 0 | null | false |
| 0 | 2020-04-25 00:34:34 | null | null | WRITE | [mode -> ErrorIfE... | null | null | null | null | null | true |
spark.sql("VACUUM delta.`{0}`".format(delta_table_path)).show()spark.Sql($"VACUUM delta.`{deltaTablePath}`").Show();spark.sql(s"VACUUM delta.`$deltaTablePath`").show()Results in:
| path |
|---|
| abfss://data@arca... |
Now, you're going to verify that a table is not a delta format table. Then, you will convert the table to delta format using Spark SQL and confirm that it was converted correctly.
parquet_id = random.randint(0,1000)parquet_path = "/parquet/parquet-table-{0}-{1}".format(session_id, parquet_id)data = spark.range(0,5)data.write.parquet(parquet_path)DeltaTable.isDeltaTable(spark, parquet_path)spark.sql("CONVERT TO DELTA parquet.`{0}`".format(parquet_path))DeltaTable.isDeltaTable(spark, parquet_path)var parquetId = (new Random()).Next(10000000);var parquetPath = $"/parquet/parquet-table-{sessionId}-{parquetId}";var data = spark.Range(0,5);data.Write().Parquet(parquetPath);DeltaTable.IsDeltaTable(parquetPath);spark.Sql($"CONVERT TO DELTA parquet.`{parquetPath}`");DeltaTable.IsDeltaTable(parquetPath);val parquetId = scala.util.Random.nextInt(1000)val parquetPath = s"/parquet/parquet-table-$sessionId-$parquetId"val data = spark.range(0,5)data.write.parquet(parquetPath)DeltaTable.isDeltaTable(parquetPath)spark.sql(s"CONVERT TO DELTA parquet.`$parquetPath`")DeltaTable.isDeltaTable(parquetPath)Results in:
True
For full documentation, see the Delta Lake Documentation Page
For more information, see Delta Lake Project.
Next steps
- .NET for Apache Spark documentation
- Azure Synapse Analytics
FAQs
Does Azure Synapse support Delta Lake? ›
The current version of Delta Lake included with Azure Synapse has language support for Scala, PySpark, and . NET and is compatible with Linux Foundation Delta Lake.
How do you use Delta Lake Spark? ›- Run interactively: Start the Spark shell (Scala or Python) with Delta Lake and run the code snippets interactively in the shell.
- Run as a project: Set up a Maven or SBT project (Scala or Java) with Delta Lake, copy the code snippets into a source file, and run the project.
You can access Azure Synapse from Azure Databricks using the Azure Synapse connector, a data source implementation for Apache Spark that uses Azure Blob storage, and PolyBase or the COPY statement in Azure Synapse to transfer large volumes of data efficiently between an Azure Databricks cluster and an Azure Synapse ...
How do you implement Delta Lake in Azure? ›- Create a table.
- Upsert to a table.
- Read from a table.
- Display table history.
- Query an earlier version of a table.
- Optimize a table.
- Add a Z-order index.
- Vacuum unreferenced files.
Synapse has an open-source Spark version with built-in support for . NET, whereas Databricks has an optimised version of Spark which offers increased performance and with this allows users to select GPU-enabled clusters which will process data faster and have a higher data concurrency.
What is the difference between Azure Data Lake and Delta Lake? ›Azure Data Lake usually has multiple data pipelines reading and writing data concurrently. It's hard to keep data integrity due to how big data pipelines work (distributed writes that can be running for a long time). Delta lake is a new Spark functionality released to solve exactly this.
What is Delta Lake in spark and how can you implement it? ›Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
How can I read and write data with Delta Lake? ›How can I read and write data with Delta Lake? You can use your favorite Apache Spark APIs to read and write data with Delta Lake. See Read a table and Write to a table.
Why use Delta Lake with Spark? ›Spark With Delta Lake Now Enables Update and Delete:
Spark doesn't allow you to run update and delete statements but integrating it with Delta Lake makes it easy. Read data. Save as Delta Table. You can Update Records.
What are the main components of Azure Synapse Analytics? An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics.
How do you connect a synapse to a data lake? ›
- Open the Azure Synapse Analytics UX and go to the Manage tab.
- Under External connections, select Linked services.
- To add a linked service, select New.
- Select the Azure Data Lake Storage Gen2 tile from the list and select Continue.
- Enter your authentication credentials. ...
- Select Create when finished.
Azure Synapse uses Azure Data Lake Storage Gen2 as a data warehouse and a consistent data model that incorporates administration, monitoring and metadata management sections.
What is the difference between Databricks and Delta Lake? ›Databricks refers to Delta Lake as a data lakehouse, a data architecture that offers both storage and analytics capabilities, in contrast to the concepts for data lakes, which store data in native format, and data warehouses, which store structured data (often in SQL format).
What is Delta Lake in Spark? ›Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks Lakehouse Platform. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling.
How do I load data into Delta Lake? ›- Run your first ETL workload. ...
- Auto Loader. ...
- Automate ETL with Delta Live Tables and Auto Loader. ...
- Upload local data files or connect external data sources. ...
- Partner integrations. ...
- COPY INTO. ...
- Convert to Delta.
Since Databricks presents itself more as a data lake than a data warehouse, it wins against Azure Synapse when it comes to use cases like ETL processes, data science, and Machine Learning. Azure Databricks is operating with advancements to the spark engine, cross-platform compatibility, and a mature workspace.
Does Azure synapse use Spark? ›Apache Spark in Azure Synapse Analytics is one of Microsoft's implementations of Apache Spark in the cloud. Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage.
Why is Databricks faster than Apache Spark? ›The Databricks Runtime implements the open Apache Spark with a highly optimized execution engine, which provides significant performance gains compared to standard open source Apache Spark found on cloud platforms.
Is Delta Lake an ETL tool? ›Get started with your Delta Lake on Databricks data transformation journey. Matillion provides a simple, powerful, and easy-to-use ETL tool that enables your company to rapidly ingest and transform raw data in Delta Lake on Databricks to analytics-ready data and, ultimately, meaningful information.
What is the purpose of Delta Lake? ›Delta Lake is an open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance. It supports ACID transactions, scalable metadata, unified streaming, and batch data processing.
How does Delta Lake work? ›
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks Lakehouse Platform. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling.
Can I use Delta Lake without Databricks? ›You no longer need Spark to interact with and use Delta Lake. Below is a summary of how everyone uses Delta Lake today, sure it's open source and you technically don't need to use Databricks … but the reality is, below is what is happening today.
When should we use Delta Lake? ›- Prevent Data Corruption.
- Faster Queries.
- Increase Data Freshness.
- Reproduce ML Models.
- Achieve Compliance.
All data in Delta Lake is stored in open Apache Parquet format, allowing data to be read by any compatible reader. APIs are open and compatible with Apache Spark. With Delta Lake on Databricks, you have access to a vast open source ecosystem and avoid data lock-in from proprietary formats.
How does Delta Lake work internally? ›Delta Lake: Basic Mechanics
When you create a new table, Delta saves your data as a series of Parquet files and also creates the _delta_log folder, which contains the Delta Lake transaction log. The ACID transaction log serves as a master record of every change (known as a transaction) ever made to your table.
What format does Delta Lake use to store data? Delta Lake uses versioned Parquet files to store your data in your cloud storage. Apart from the versions, Delta Lake also stores a transaction log to keep track of all the commits made to the table or blob store directory to provide ACID transactions.
What is the difference between Delta table and Delta Lake? ›A Databricks Delta Table records version changes or modifications in a feature class of table in Delta Lake. Unlike traditional tables that store data in a row and column format, the Databricks Delta Table facilitates ACID transactions and time travel features to store metadata information for quicker Data Ingestion.
How does Databricks work with Spark? ›Databricks incorporates an integrated workspace for exploration and visualization so users can learn, work, and collaborate in a single, easy to use environment. You can easily schedule any existing notebook or locally developed Spark code to go from prototype to production without re-engineering.
What is one of the key advantages with using Spark for analytics? ›One of the main features Spark offers for speed is the ability to run computations in memory, but the system is also more efficient than MapReduce for complex applications running on disk.
Is Azure Synapse Analytics an ETL tool? ›Azure Synapse and Snowflake are two commonly recommended ETL tools for businesses that need to process large amounts of data.
What is the difference between Lake database and SQL database in Azure Synapse? ›
What are the differences between Lake Databases and SQL Serverless databases? At face value, they're very similar. They're both means by which you can query data in your data lake. However, Lake Databases are special in that they're synchronized between the Spark and the SQL Serverless engines in Synapse.
What are the 2 concepts that Azure Synapse brings together? ›Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics.
How do I connect to Azure synapse Analytics? ›- Go to the Azure portal.
- Select on Synapse workspaces.
- Select on the workspace you want to connect to.
- Go to overview.
- Locate the full server name.
Connect to MicroStrategy Web and click Add External Data from the Home page. Select Azure Synapse Analytics from the Connect to Your Data window. Select a query type from the Select Import Options window. Click the icon next to Data Sources to add a new data source.
How do you load data into azure synapse? ›- Select + New connection.
- Select Azure SQL Database from the gallery, and select Continue. ...
- In the New connection (Azure SQL Database) page, select your server name and DB name from the dropdown list, and specify the username and password.
Azure Synapse is more suited for data analysis and for those users familiar with SQL. Databricks is more suited to streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables use of multiple languages. It isn't really a data warehouse at all.
What is the difference between data lake and data warehouse in Azure Synapse? ›While a data lake holds data of all structure types, including raw and unprocessed data, a data warehouse stores data that has been treated and transformed with a specific purpose in mind, which can then be used to source analytic or operational reporting.
Is Azure synapse same as Synapse analytics? ›Azure SQL DW was rebranded as “Dedicated SQL pool (formerly SQL DW)” with intention to create clear indication that the former SQL DW is in fact the same artifact that lives within Synapse Analytics.
What is the difference between data warehouse and Delta Lake? ›A data lake contains all an organization's data in a raw, unstructured form, and can store the data indefinitely — for immediate or future use. A data warehouse contains structured data that has been cleaned and processed, ready for strategic analysis based on predefined business needs.
Can Delta Lake replace data warehouse? ›While Delta Lake can store and process data faster and easier than a relational data warehouse and can scale better, it is not a replacement for a data warehouse as it is not as robust and performant, among other reasons (see Is the traditional data warehouse dead?) .
What are the elements of Delta Lake? ›
Key Features of Delta Lake are: ACID Transactions (Atomicity, Consistency, Isolation, Durability) – With Delta you don't need to write any code – it's automatic that transactions are written to the log. This transaction log is the key, and it represents a single source of truth.
What is a Delta Lake and how does it offer a solution to building reliable data pipelines? ›Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing.
Does synapse support delta lake? ›The current version of Delta Lake included with Azure Synapse has language support for Scala, PySpark, and . NET and is compatible with Linux Foundation Delta Lake.
Should I create or replace table in Delta Lake? ›Databricks strongly recommends using REPLACE instead of dropping and re-creating Delta Lake tables. If specified and a table with the same name already exists, the statement is ignored. IF NOT EXISTS cannot coexist with REPLACE , which means CREATE OR REPLACE TABLE IF NOT EXISTS is not allowed.
How do you access data from data lake? ›- Mount an Azure Data Lake Storage Gen2 filesystem to DBFS using a service principal and OAuth 2.0.
- Use a service principal directly.
- Use the Azure Data Lake Storage Gen2 storage account access key directly.
Limit is 8000 for char data types, 4000 for nvarchar, or 2 GB for MAX data types. The number of bytes per row is calculated in the same manner as it is for SQL Server with page compression. Like SQL Server, row-overflow storage is supported, which enables variable length columns to be pushed off-row.
Is Azure Synapse A data Lakehouse? ›A Data Lakehouse is a modern architecture that combines Data Lake and Data Warehouse. In the Azure Cloud there are several possibilities to build such a Data Lakehouse — one of them is the use of Azure Synapse.
Does Azure Databricks include Delta Lake? ›Delta Lake is the default storage format for all operations on Azure Databricks. Unless otherwise specified, all tables on Azure Databricks are Delta tables. Databricks originally developed the Delta Lake protocol and continues to actively contribute to the open source project.
Which are three components of Azure Synapse Analytics? ›What are the main components of Azure Synapse Analytics? An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics.
What is the relationship between Databricks SQL and Delta Lake? ›Delta Lake is the default storage format for all operations on Databricks. Unless otherwise specified, all tables on Databricks are Delta tables. Databricks originally developed the Delta Lake protocol and continues to actively contribute to the open source project.
How do I link my Azure Synapse to data lake? ›
- Open the Azure Synapse Analytics UX and go to the Manage tab.
- Under External connections, select Linked services.
- To add a linked service, select New.
- Select the Azure Data Lake Storage Gen2 tile from the list and select Continue.
- Enter your authentication credentials. ...
- Select Create when finished.
Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.