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Renita Lukins

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Aug 3, 2024, 3:44:02 PM8/3/24

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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.

Data Lake Storage Gen2 makes Azure Storage the foundation for building enterprise data lakes on Azure. Designed from the start to service multiple petabytes of information while sustaining hundreds of gigabits of throughput, Data Lake Storage Gen2 allows you to easily manage massive amounts of data.

A data lake is a single, centralized repository where you can store all your data, both structured and unstructured. A data lake enables your organization to quickly and more easily store, access, and analyze a wide variety of data in a single location. With a data lake, you don't need to conform your data to fit an existing structure. Instead, you can store your data in its raw or native format, usually as files or as binary large objects (blobs).

Azure Data Lake Storage is a cloud-based, enterprise data lake solution. It's engineered to store massive amounts of data in any format, and to facilitate big data analytical workloads. You use it to capture data of any type and ingestion speed in a single location for easy access and analysis using various frameworks.

Azure Data Lake Storage Gen2 refers to the current implementation of Azure's Data Lake Storage solution. The previous implementation, Azure Data Lake Storage Gen1 will be retired on February 29, 2024.

Unlike Data Lake Storage Gen1, Data Lake Storage Gen2 isn't a dedicated service or account type. Instead, it's implemented as a set of capabilities that you use with the Blob Storage service of your Azure Storage account. You can unlock these capabilities by enabling the hierarchical namespace setting.

Azure Data Lake Storage Gen2 is primarily designed to work with Hadoop and all frameworks that use the Apache Hadoop Distributed File System (HDFS) as their data access layer. Hadoop distributions include the Azure Blob File System (ABFS) driver, which enables many applications and frameworks to access Azure Blob Storage data directly. The ABFS driver is optimized specifically for big data analytics. The corresponding REST APIs are surfaced through the endpoint dfs.core.windows.net.

Data analysis frameworks that use HDFS as their data access layer can directly access Azure Data Lake Storage Gen2 data through ABFS. The Apache Spark analytics engine and the Presto SQL query engine are examples of such frameworks.

The hierarchical namespace is a key feature that enables Azure Data Lake Storage Gen2 to provide high-performance data access at object storage scale and price. You can use this feature to organize all the objects and files within your storage account into a hierarchy of directories and nested subdirectories. In other words, your Azure Data Lake Storage Gen2 data is organized in much the same way that files are organized on your computer.

Operations such as renaming or deleting a directory, become single atomic metadata operations on the directory. There's no need to enumerate and process all objects that share the name prefix of the directory.

Azure Data Lake Storage Gen2 is priced at Azure Blob Storage levels. It builds on Azure Blob Storage capabilities such as automated lifecycle policy management and object level tiering to manage big data storage costs.

Performance is optimized because you don't need to copy or transform data as a prerequisite for analysis. The hierarchical namespace capability of Azure Data Lake Storage allows for efficient access and navigation. This architecture means that data processing requires fewer computational resources, reducing both the speed and cost of accessing data.

The Azure Data Lake Storage Gen2 access control model supports both Azure role-based access control (Azure RBAC) and Portable Operating System Interface for UNIX (POSIX) access control lists (ACLs). There are also a few extra security settings that are specific to Azure Data Lake Storage Gen2. You can set permissions either at the directory level or at the file level. All stored data is encrypted at rest by using either Microsoft-managed or customer-managed encryption keys.

Azure Data Lake Storage Gen2 offers massive storage and accepts numerous data types for analytics. It doesn't impose any limits on account sizes, file sizes, or the amount of data that can be stored in the data lake. Individual files can have sizes that range from a few kilobytes (KBs) to a few petabytes (PBs). Processing is executed at near-constant per-request latencies that are measured at the service, account, and file levels.

The data that you ingest persist as blobs in the storage account. The service that manages blobs is the Azure Blob Storage service. Data Lake Storage Gen2 describes the capabilities or "enhancements" to this service that caters to the demands of big data analytic workloads.

Because these capabilities are built on Blob Storage, features such as diagnostic logging, access tiers, and lifecycle management policies are available to your account. Most Blob Storage features are fully supported, but some features might be supported only at the preview level and there are a handful of them that aren't yet supported. For a complete list of support statements, see Blob Storage feature support in Azure Storage accounts. The status of each listed feature will change over time as support continues to expand.

The Azure Blob Storage table of contents features two sections of content. The Data Lake Storage Gen2 section of content provides best practices and guidance for using Data Lake Storage Gen2 capabilities. The Blob Storage section of content provides guidance for account features not specific to Data Lake Storage Gen2.

As you move between sections, you might notice some slight terminology differences. For example, content featured in the Blob Storage documentation, will use the term blob instead of file. Technically, the files that you ingest to your storage account become blobs in your account. Therefore, the term is correct. However, the term blob can cause confusion if you're used to the term file. You'll also see the term container used to refer to a file system. Consider these terms as synonymous.

I have a lot of ADLS gen2 storage accounts. I find that Azure Data Explorer doesn't have the little view-options button to the right of the address bar -- although it is still present and works correctly for gen 1 storage accounts.

This is not a Rhino thing. The graphics cards 2080 etc need a lot of cooling and when you put them in a thin laptop you get noisy fans. Its the price you pay for great cards in thin devices. Same happens to me with an MSI using 2080 on most 3D software.

As MIchael said, this is the price you pay for a powerfull graphic card (so far): fan noise!
very annoying
I own a p53 with a rtx5000 and the same goes here. Lenovo technical support said that this was expected (asked when laptop was brand new).
I manually reduce windows performance settings when not working on demanding tasks. Noise cancellation earphones helps also ;)l

The cooling system is chunky with dual fans and a plethora of heat pipes, but the laptop still runs hot and throttles up to 20% under full load. Those two fans are also quite loud when spun up, hitting between 50 and 60 decibels at full tilt. source: Lenovo ThinkPad P15 (Gen 2) review: Outdated looks hide high-end workstation performance and features Windows Central

Correct me if I am wrong, but I believe that buying powerful laptop is a waste of money. You can reduce its fan noise by forcing its CPU and GPU to run slowly (throttle), but it seems more rational to buy slow, cheap laptop instead.

Though, you might check the nVidia Control Panel settings, and set your power management to Adoptive, or Normal. If it is in High Performance, the responsiveness will be better, but they GPU will generally not get a chance to cool down enough to be free from thermal soak enough to handle bursty work.

Also, you have the fastest Quad that Lenovo put in a computer, at the time. Are you sure that you have a 2080? Generally, Lenovo uses Quado chips in their P series, which have nearly but not entirely Geforce equivalents. Notwithstanding, the fact that you have a MaxQ, means that you have more GPU than cooling, so Adoptive cooling profiles help.

It angers me to no end that I have a desktop for rendering. For the love of money, why cannot I have a laptop that can render day after day, like my desktop can? Why do I have to worry about my laptop overheating? The difference: cooling and airflow.

Hi. I think the URL you are using is wrong. If you go to the storage explorer inside Azure Portal from Web you can find the correct url in properties. It looks like this:
[company].dfs.core.windows.net/[container]/[folder]/[file]

Thanks! You are absolutely right. I used the url in the properties of the container. When I switched to the url listed as Primary Endpoint in the properties of the data lake itself it works.
You made my day! Thanks again

I am using ADLS Gen2, from a Databricks notebook trying to process the file using 'abfss' path.I am able to read parquet files just fine but when I try to load the XML files, I am getting the error the configuration is not found - Configuration property xxx.dfs.core.windows.net not found.

The package com.databricks:spark-xml seems using RDD API to read xml file. When we use using the RDD API to access Azure Data Lake Storage Gen2, wecannot access Hadoop configuration options set using spark.conf.set(...). So we should update the code as spark._jsc.hadoopConfiguration().set("fs.azure.account.key.xxxxx.dfs.core.windows.net", "xxxx=="). For more details, please refer to here.