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Category: Data Lake

Creating Delta Lake Tables in Azure Databricks

Gauri Mahajan takes us through creating new tables in a Delta Lake using Azure Databricks:

Delta lake is an open-source data format that provides ACID transactions, data reliability, query performance, data caching and indexing, and many other benefits. Delta lake can be thought of as an extension of existing data lakes and can be configured per the data requirements. Azure Databricks has a delta engine as one of the core components that facilitates delta lake format for data engineering and performance. Delta lake format is used to create modern data lake or lakehouse architectures. It is also used to build a combined streaming and batch architecture popularly known as lambda architecture.

Click through for the process.

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SCD Type 2 with Delta Lake

Chris Williams continues a series on slowly changing dimensions in Delta Lake:

Type 2 SCD is probably one of the most common examples to easily preserve history in a dimension table and is commonly used throughout any Data Warehousing/Modelling architecture. Active rows can be indicated with a boolean flag or a start and end date. In this example from the table above, all active rows can be displayed simply by returning a query where the end date is null.

Read on to see how you can implement this pattern using Delta Lake’s capabilities.

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Change Data Capture in Delta Lake

Surya Sai Turaga and John O’Dwyer take us through change data capture in Delta Lake:

Change data capture (CDC) is a use case that we see many customers implement in Databricks – you can check out our previous deep dive on the topic here. Typically we see CDC used in an ingestion to analytics architecture called the medallion architecture. The medallion architecture that takes raw data landed from source systems and refines the data through bronze, silver and gold tables. CDC and the medallion architecture provide multiple benefits to users since only changed or added data needs to be processed. In addition, the different tables in the architecture allow different personas, such as Data Scientists and BI Analysts, to use the correct up-to-date data for their needs. We are happy to announce the exciting new Change Data Feed (CDF) feature in Delta Lake that makes this architecture simpler to implement and the MERGE operation and log versioning of Delta Lake possible!

Read on to gain an understanding of how it works.

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Data Hubs, Warehouses, and Lakes

Trevor Legg compares and contrasts data hubs, data warehouses, and data lakes:

Data hubs, data warehouses, and data lakes are significant investment areas for data and analytics leaders and are vital to support increasingly complex, distributed, and varied data workloads.

Gartner finds that 57% of data and analytics leaders are investing in data warehouses, 46% are using data hubs, and 39% are using data lakes. However, they also found that these same data and analytics leaders don’t necessarily understand the difference between the three…

To best support specific business requirements, it’s vital to understand the difference and purpose of each type of structure, and the role it can play in modern data management infrastructure.

Click through for the definitions and comparisons.

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Reading Delta Lake Tables from Power BI

Gerhard Brueckl checks out the Apache Parquet connector in Power BI, reading from a Delta Lake:

“Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.”

However, Parquet is just a file format and does not really support you when it comes to data management. Common data manipulation operations (DML)  like updates and deletes still need to be handled manually by the data pipeline. This was one of the reasons why Delta Lake (delta.io) was developed besides a lot of other features like ACID transactions, proper meta data handling and a lot more. If you are interested in the details, please follow the link above.

Click through for a demo.

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Living in the Lakehouse

James Serra defines the term “data lakehouse”:

As a follow-up to my blog Data Lakehouse & Synapse, I wanted to talk about the various definitions I am seeing about what a data lakehouse is, including a recent paper by Databricks.

Databricks uses the term “Lakehouse” in their paper (see Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics), which argues that the data warehouse architecture as we know it today will wither in the coming years and be replaced by a new architectural pattern, the Lakehouse. Instead of the two-tier data lake + relational data warehouse model, you will just need a data lake, which is made possible by implementing data warehousing functionality over open data lake file formats.

While I agree there may be some uses cases where technical designs may allow Lakehouse systems to completely replace relational data warehouses, I believe those use cases are much more limited than this paper suggests.

James is a sharp and perceptive fellow, so read the whole thing.

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Q&A about the Lakehouse

Terry McCann posts Q&A from Simon Whiteley’s session on Lakehouse models in Spark 3.0:

“WHILE ALL THE HADOOP PROVIDERS PROMOTED THE DATALAKE PARADIGM BACK THEN, HOW THE INDUSTRY AND THE OTHER DATA LAKE PROVIDERS ARE SHIFTING TO/CONSIDERING THE LAKE HOUSE PARADIGM?“

It’s a direction that most providers are heading in, albeit under the “unified analytics” or “modern warehouse” name rather than the “lakehouse”. But most big relational engines are moving to bring in spark/big data capabilities, other lake providers are looking to expand their SQL coverage. It’s a bit of a race to who gets to the “can do both sides as well as a specialist tool” point first. Will we see other tools championing it as a “lakehouse”, or is that term now tied too closely as a “vendor-specific” term coming from Databricks? We’ll see…

Click through for some good questions and thoughtful answers.

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The Evolving Lakehouse

Simon Whiteley looks at the current status of the Lakehouse model:

We have discussed in the past this idea of the lakehouse, the aspirational target of many analytics platforms these days of combining the huge power and potential of data lakes with the rigour, reliability and concurrency of a data warehouse. It’s an interesting concept but has, in the past, been firmly an aspiration.

In the world without lakehouses, we often see the “Modern Data Warehouse”, this two-phased approach to providing a holistic platform – we load our early data into a lake where we shape it and massage it into an understandable state. It is here we perform data science, exploratory data analysis, early sight analytics prototyping and various other functions that don’t quite fit into a data warehouse… but then we load our data into a relational store for serving to the business. This is where we can meet their demands for a rich SQL environment, auditable data models and rigorous change procedures. Essentially, we store data twice so that we can achieve the best of both worlds.

Definitely read Simon’s take on it. My take is that the Lakehouse concept will start to be useful to specific companies in about 2-3 years, as I don’t think the performance is there today.

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Querying Data Lake Files in Power BI through Synapse Analytics

Wolfgang Strasser shows us how to integrate Azure Synapse Analytics and Power BI:

Sometimes however, would not it be nice to access the data lake in Direct Query mode – to get the most up to date information for every report view? I would say: yes … but how can you achieve this? The options natively provided by ADLS Gen2 and Power BI are not sufficient to solve this requirement. But: there are options to achieve this and, in this post, I would like to show you the possibilities using Azure Synapse Analytics to build a query layer on top of a ADLS Gen2 storage account.

Click through for a step-by-step walkthrough.

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