The SPNs/MSIs for ADF as well as the users and the service engineering team can be added to the LogsWriter group. Where your choose to store your logs from Azure Storage logs becomes important when you consider how you will access it: If you want to access your logs in near real-time and be able to correlate events in logs with other metrics from Azure Monitor, you can store your logs in a Log Analytics workspace. In general, its a best practice to organize your data into larger sized files (target at least 100 MB or more) for better performance. This includes: All of the telemetry for your data lake is available through Azure Storage logs in Azure Monitor. If you do not require isolation and you are not utilizing your storage accounts to their fullest capabilities, you will be incurring the overhead of managing multiple accounts without a meaningful return on investment. When using RBAC at the container level as the only mechanism for data access control, be cautious of the 2000 limit, particularly if you are likely to have a large number of containers. The data in its natural form is stored as raw data, and schema and transformations are applied on this raw data to gain valuable business insights depending on the key questions the business is trying to answer. A file has only access ACLs and no default ACLs. This data is stored as is in the data lake and is consumed by an analytics engine such as Spark to perform cleansing and enrichment operations to generate the curated data. The following table shows the main Azure services you can use to build your data lake architecture. A data lake solution in Azure typically consists of four building blocks. Analytics engines (your ingest or data processing pipelines) incur an overhead for every file they read (related to listing, checking access and other metadata operations) and too many small files can negatively affect the performance of your overall job. A subscription is associated with limits and quotas on Azure resources, you can read about them here. For more details, see: Optimize for high throughput target getting at least a few MBs (higher the better) per transaction. Some customers have end to end ownership of the components of an analytics pipeline, and other customers have a central team/ organization managing the infrastructure, operations and governance of the data lake while serving multiple customers either other organizations in their enterprise or other customers external to their enterprise. It provides a platform for .NET developers to effectively process up to petabytes of data. Parquet is one such prevalent data format that is worth exploring for your big data analytics pipeline. While ADLS Gen2 storage is not very expensive and lets you store a large amount of data in your storage accounts, lack of lifecycle management policies could end up growing the data in the storage very quickly even if you dont require the entire corpus of data for your scenarios. One common question that our customers ask is if a single storage account can infinitely continue to scale to their data, transaction and throughput needs. Beyond this, organizations can optionally use Azure Data Lake Storage, a specialized storage service for large-scale datasets, and Azure Data Lake Analytics, a compute service that processes large scale data sets using T-SQL. As we continue to work with our customers to unlock key insights out of their data using ADLS Gen2, we have identified a few key patterns and considerations that help them effectively utilize ADLS Gen2 in large scale Big Data platform architectures. If you want to store your logs for both near real-time query and long term retention, you can configure your diagnostic settings to send logs to both a Log Analytics workspace and a storage account. Storage account: An Azure resource that contains all of your Azure Storage data objects: blobs, files, queues, tables and disks. You can read more about our data lifecycle management policies to identify a plan that works for you. In this case, you would want to optimize for the organization by date and attribute over the sensorID. Azure Storage logs in Azure Monitor is a new preview feature for Azure Storage which allows for a direct integration between your storage accounts and Log Analytics, Event Hubs, and archival of logs to another storage account utilizing standard diagnostic settings. In most cases, you should have the region in the beginning of your directory structure, and the date at the end. Query acceleration lets you filter for the specific rows and columns of data that you want in your dataset by specifying one more predicates (think of these as similar to the conditions you would provide in your WHERE clause in a SQL query) and column projections (think of these as columns you would specify in the SELECT statement in your SQL query) on unstructured data. all the data in the past 12 hours), the partitioning scheme (in this case, done by datetime) lets you skip over the irrelevant data and only seek the data that you want. Factors to consider when picking the option that works for you. This document captures these considerations and best practices that we have learnt based on working with our customers. Parquet and ORC file formats are favored when the I/O patterns are more read heavy and/or when the query patterns are focused on a subset of columns in the records where the read transactions can be optimized to retrieve specific columns instead of reading the entire record. Open source computing frameworks such as Apache Spark provide native support for partitioning schemes that you can leverage in your big data application. As a pre-requisite to optimizations, it is important for you to understand more about the transaction profile and data organization. Cross resource RBACs at subscription or resource group level. What are the various analytics workloads that Im going to run on my data lake? Services such as Azure Synapse Analytics, Azure Databricks and Azure Data Factory have native functionality built in to take advantage of Parquet file formats as well. Folder structures mirror teams that the workspace is used by. if you have a Spark job reading all sales data of a product from a specific region for the past 3 months, then an ideal folder structure here would be /enriched/product/region/timestamp. It lets you leverage these open source projects, with fully managed infrastructure and cluster management, and no need for installation and customization. Consider the workload's target recovery time objective (RTO) and recovery point objective (RPO). Avro file format is favored where the I/O patterns are more write heavy or the query patterns favor retrieving multiple rows of records in their entirety. Azure Data Lake Storage Gen2 provides Portable Operating System Interface (POSIX) access control for users, groups, and service principals defined in Azure Active Directory (Azure AD). If you are not able to pick an option that perfectly fits your scenarios, we recommend that you do a proof of concept (PoC) with a few options to let the data guide your decision. As our enterprise customers serve the needs of multiple organizations including analytics use-cases on a central data lake, their data and transactions tend to increase dramatically. What are the various transaction patterns on the analytics workloads? It allows users to run popular open source frameworks such as Apache Hadoop, Spark, and Kafka. Create different folders or containers (more below on considerations between folders vs containers) for the different data zones - raw, enriched, curated and workspace data sets. Archive data: This is your organizations data vault - that has data stored to primarily comply with retention policies and has very restrictive usage, such as supporting audits. Azure HDInsight is a managed service for running distributed big data jobs on Azure infrastructure. While at a higher level, they both are used for logical organizations of the data, they have a few key differences. A very common point of discussion as we work with our customers to build their data lake strategy is how they can best organize their data. Another optional component is Azure HDInsight, which lets you run distributed big data jobs using tools like Hadoop and Spark. This is part of our series of articles onAzure big data. Who needs access to what parts of my data lake? Following this practice will help you minimize the process of managing access for new identities which would take a really long time if you want to add the new identity to every single file and folder in your container recursively. These RBACs apply to all data inside the container. NetApp Cloud Volumes ONTAP, the leading enterprise-grade storage management solution, delivers secure, proven storage management services on AWS, Azure and Google Cloud. However, when we talk about optimizing your data lake for performance, scalability and even cost, it boils down to two key factors :-. For specific security principals you want to provide permissions, add them to the security group instead of creating specific ACLs for them. When is ADLS Gen2 the right choice for your data lake? It lets you store data in two ways: Azure Data Lake Analytics is a compute service that lets you connect and process data from ADLS. As we have already talked about, optimizing your storage I/O patterns can largely benefit the overall performance of your analytics pipeline. Depending on the retention policies of your enterprise, this data is either stored as is for the period required by the retention policy or it can be deleted when you think the data is of no more use. This allows you to query your logs using KQL and author queries which enumerate the. Let us look at some common file formats Avro, Parquet and ORC. The pricing for ADLS Gen2 can be found here. a retail customer can store the past 5 years worth of sales data in a data lake, and in addition they can process data from social media to extract the new trends in consumption and intelligence from retail analytics solutions on the competitive landscape and use all these as input together to generate a data set that can be used to project the next years sales targets. Given the varied nature of analytics scenarios, the optimizations depend on your analytics pipeline, storage I/O patterns and the data sets you operate on, specifically the following aspects of your data lake. Its worth noting that we have seen customers have different definition of what hyperscale means this depends on the data stored, the number of transactions and the throughput of the transactions. Azure Data Lake Storage Gen2 (ADLS Gen2) is a highly scalable and cost-effective data lake solution for big data analytics. In addition, Cloud Volumes ONTAP provides storage efficiency features, including thin provisioning, data compression, and deduplication, reducing the storage footprint and costs by up to 70%. Azure Data Lake is a big data solution based on multiple cloud services in the Microsoft Azure ecosystem. While ADLS Gen2 supports storing all kinds of data without imposing any restrictions, it is better to think about data formats to maximize efficiency of your processing pipelines and optimize costs you can achieve both of these by picking the right format and the right file sizes. E.g. Learn more about building a cloud data lake here: Cloud Data Lake in 5 Steps. Multiple storage accounts provide you the ability to isolate data across different accounts so different management policies can be applied to them or manage their billing/cost logic separately. ADLS Gen2 offers a data lake store for your analytics scenarios with the goal of lowering your total cost of ownership. As an enterprise data lake, you have two available options either centralize all the data management for your analytics needs within one organization, or have a federated model, where your customers manage their own data lakes while the centralized data team provides guidance and also manages a few key aspects of the data lake such as security and data governance. It also uses Apache Hadoop YARN as a cluster management platform, which can manage scalability of SQL Server instances, Azure SQL Database instances, and Azure SQL Data Warehouse servers. E.g. Cloud Volumes ONTAP supports advanced features for managing SAN storage in the cloud, catering for NoSQL database systems, as well as NFS shares that can be accessed directly from cloud big data analytics clusters. Let us take our Contoso.com example where they have analytics scenarios to manage the company operations. Use access control to create default permissions that can be automatically applied to new files or directories. Let us put these aspects in context with a few scenarios. Start your design approach with one storage account and think about reasons why you need multiple storage accounts (isolation, region based requirements etc) instead of the other way around. ADLS Gen2 offers faster performance and Hadoop compatible access with the hierarchical namespace, lower cost and security with fine grained access controls and native AAD integration. a Data Science team is trying to determine the product placement strategy for a new region, they could bring other data sets such as customer demographics and data on usage of other similar products from that region and use the high value sales insights data to analyze the product market fit and the offering strategy. Storage accounts, containers. You can also use this opportunity to store data in a read-optimized format such as Parquet for downstream processing. Driven by global markets and/or geographically distributed organizations, there are scenarios where enterprises have their analytics scenarios factoring multiple geographic regions. How much data am I storing in the data lake? As our enterprise customers build out their data lake strategy, one of the key value proposition of ADLS Gen2 is to serve as the single data store for all their analytics scenarios. Data may arrive to your data lake account in a variety of formats human readable formats such as JSON, CSV or XML files, compressed binary formats such as .tar.gz and a variety of sizes huge files (a few TBs) such as an export of a SQL table from your on-premise systems or a large number of tiny files (a few KBs) such as real-time events from your IoT solution. Folder can contain other folders or files. In addition, since the similar data types (for a column) are stored together, Parquet lends itself friendly to efficient data compression and encoding schemes lowering your data storage costs as well, compared to storing the same data in a text file format. For at-scale deployments, Azure Policy can be used with full support for remediation tasks. Avro format is favored by message bus such as Event Hub or Kafka writes multiple events/messages in succession. Data that needs to be isolated to a region E.g. Consider the analytics consumption patterns when designing your folder structures. A data lake is a store for all types of data from various sources. Given this is customer data, there are sovereignty requirements that need to be met, so the data cannot leave the region. You can find more examples and scenarios on directory layout in our. If you want to optimize for ease of management, specially if you adopt a centralized data lake strategy, this would be a good model to consider. data science notebooks) or through a data warehouse. Key considerations in designing your data lake, Organizing and managing data in your data lake. The goal of the enterprise data lake is to eliminate data silos (where the data can only be accessed by one part of your organization) and promote a single storage layer that can accommodate the various data needs of the organization For more information on picking the right storage for your solution, please visit the Choosing a big data storage technology in Azure article. This creates a management problem of what is the source of truth and how fresh it needs to be, and also consumes transactions involved in copying data back and forth. You can read more about these policies, Ensure that you are choosing the right replication option for your accounts, you can read the, Being able to audit your data lake in terms of frequent operations, Having visibiliy into key performace indicators such as operations with high latency, Undestanding common errors, the operations that caused the error, and operations which cause service-side throttling. Its worth noting that while all this data layers are present in a single logical data lake, they could be spread across different physical storage accounts. Inside a zone, choose to organize data in folders according to logical separation, e.g. Please remember that this single data store is a logical entity that could manifest either as a single ADLS Gen2 account or as multiple accounts depending on the design considerations. In addition to ensuring that there is enough isolation between your development and production environments requiring different SLAs, this also helps you track and optimize your management and billing policies efficiently. This is not an official HOW-TO documentation. Virtual machines, storage accounts, VNETs are examples of resources. E.g. A file has an access control list associated with it. Optimize data access patterns reduce unnecessary scanning of files, read only the data you need to read. This lends itself as the choice for your enterprise data lake focused on big data analytics scenarios extracting high value structured data out of unstructured data using transformations, advanced analytics using machine learning or real time data ingestion and analytics for fast insights. Data organization in a an ADLS Gen2 account can be done in the hierarchy of containers, folders and files in that order, as we saw above. hbspt.cta._relativeUrls=true;hbspt.cta.load(525875, 'b940696a-f742-4f02-a125-1dac4f93b193', {"useNewLoader":"true","region":"na1"}); Azure Big Data: 3 Steps to Building Your Solution, Azure NoSQL: Types, Services, and a Quick Tutorial, Azure Analytics Services: An In-Depth Look, Azure Data Lake: 4 Building Blocks and Best Practices, This is part of our series of articles on, Best Practices for Using Azure HDInsight for Big Data and Analytics, Building Your Azure Data Lake: Complementary Services, Azure Data Lake with NetApp Cloud Volumes ONTAP. Consider the access control model you would want to follow when deciding your folder structures. An effective paritioning scheme for your data can imrpove the performance of your analytics pipeline and also reduce the overall transaction costs incurred with your query. log messages from servers) or aggregate it (E.g. In another scenario, enterprises that serve as a multi-tenant analytics platform serving multiple customers could end up provisioning individual data lakes for their customers in different subscriptions to help ensure that the customer data and their associated analytics workloads are isolated from other customers to help manage their cost and billing models. When deciding the structure of your data, consider both the semantics of the data itself as well as the consumers who access the data to identify the right data organization strategy for you. ACLs let you manage a specific set of permissions for a security principal to a much narrower scope a file or a directory in ADLS Gen2. Resource: A manageable item that is available through Azure. As an example, let us follow the journey of sales data as it travels through the data analytics platform of Contoso.com. A single storage account gives you the ability to manage a single set of control plane management operations such as RBACs, firewall settings, data lifecycle management policies for all the data in your storage account, while allowing you to organize your data using containers, files and folders on the storage account. This section provides key considerations that you can use to manage and optimize the cost of your data lake. Related content: read our guide to Azure Big Data solutions. When deciding the number of storage accounts you want to create, the following considerations are helpful in deciding the number of storage accounts you want to provision. In the meantime, while we call out specific engines as examples, please do note that these samples talk primarily about storage performance. The data in the raw zone is sometimes also stored as an aggregated data set, e.g. Azure Data Lake Storage has a capability called Query Acceleration available in preview that is intended to optimize your performance while lowering the cost. Let us take an example of an IoT scenario at Contoso where data is ingested real time from various sensors into the data lake. It is important to remember that both the centralized and federated data lake strategies can be implemented with one single storage account or multiple storage accounts. Another common questions that our customers ask if when to use containers and when to use folders to organize the data. We urge you to think about data lake and data warehouse as complementary solutions that work together to help you derive key insights from your data. Azure Data Lake Analytics allows users to run analytics jobs of any size, leveraging U-SQL to perform analytics tasks that combine C# and SQL. Add a data processing layer in your analytics pipeline to coalesce data from multiple small files into a large file. You can use the Cool and Archive tiers in ADLS Gen2 to store this data. If you want to access your logs through another query engine such as. In this case, they could choose to create different data lakes for the various data sources. Leverage Azures range of storage redundancy options, ranging from Local Redundant Storage (LRS) to Read-Access Geo-Redundant Storage (RA-GRS). There are no limits on how many folders or files can be created under a folder. All of these are machine-readable binary file formats, offer compression to manage the file size and are self-describing in nature with a schema embedded in the file. This organization follows the lifecycle of the data as it flows through the source systems all the way to the end consumers the BI analysts or Data Scientists. Raw data: This is data as it comes from the source systems. Contoso wants to provide a personalized buyer experience based on their profile and buying patterns. There is still one centralized logical data lake with a central set of infrastructure management, data governance and other operations that comprises of multiple storage accounts here. While technically a single ADLS Gen2 could solve your business needs, there are various reasons why a customer would choose multiple storage accounts, including, but not limited to the following scenarios in the rest of this section. What portion of your data do you run your analytics workloads on? In this scenario, the customer would provision region-specific storage accounts to store data for a particular region and allow sharing of specific data with other regions. Create different storage accounts (ideally in different subscriptions) for your development and production environments. Under construction, looking for contributions, In this section, we will address how to optimize your data lake store for your performance in your analytics pipeline. Identify the different logical sets of your data and think about your needs to manage them in a unified or isolated fashion this will help determine your account boundaries. /raw/sensordata /raw/lobappdata /raw/userclickdata, /workspace/salesBI /workspace/manufacturindatascience. ADLS Gen2 provides policy management that you can use to leverage the lifecycle of data stored in your Gen2 account. While the end consumers have control of this workspace, ensure that there are processes and policies to clean up data that is not necessary using policy based DLM for e.g., the data could build up very easily. Data that can be shared globally across all regions E.g. In a lot of cases, if your raw data (from various sources) itself is not large, you have the following options to ensure the data set your analytics engines operate on is still optimized with large file sizes. Resource group: A logical container to hold the resources required for an Azure solution can be managed together as a group. For the purposes of this document, we will be focusing on the ADLS Gen2 storage account which is essentially a Azure Blob Storage account with Hierarchical Namespace enabled, you can read more about it here. Two common patterns where we see this kind of data growth is :-. In these cases, having a metastore is helpful for discovery. Folder structure mirrors organization, e.g. In the case of processing real time data, you can use a real time streaming engine (such as Azure Stream Analytics or Spark Streaming) in conjunction with a message broker (such as Event Hub or Apache Kafka) to store your data as larger files. To ensure we have the right context, there is no silver bullet or a 12 step process to optimize your data lake since a lot of considerations depend on the specific usage and the business problems you are trying to solve. An enterprise data lake is designed to be a central repository of unstructured , semi-structured and structured data used in your big data platform. Subscription: An Azure subscription is a logical entity that is used to separate the administration and financial (billing) logic of your Azure resources. You can read more about storage accounts here. In addition to managing access using AAD identities using RBACs and ACLs, ADLS Gen2 also supports using SAS tokens and shared keys for managing access to data in your Gen2 account. With little or no centralized control, so will the associated costs increase. Enriched data: This layer of data is the version where raw data (as is or aggregated) has a defined schema and also, the data is cleansed, enriched (with other sources) and is available to analytics engines to extract high value data. E.g. We would like to anchor the rest of this document in the following structure for a few key design/architecture questions that we have heard consistently from our customers. The data itself can be categorized into two broad categories. You can read more about resource groups here. When ingesting data into a data lake, you should plan data structure to facilitate security, efficient processing and partitioning. Understanding how your data lake is used and how it performs is a key component of operationalizing your service and ensuring it is available for use by any workloads which consume the data contained within it. Do I want a centralized or a federated data lake implementation? Curated data: This layer of data contains the high value information that is served to the consumers of the data the BI analysts and the data scientists. Azure Data Lake Storage is a repository that can store massive datasets. Let us take an example where you have a directory, /logs, in your data lake with log data from your server. Putting the date at the end means that you can restrict specific date ranges without having to process many subdirectories unnecessarily. real time streaming data). RBACs can help manage roles related to control plane operations (such as adding other users and assigning roles, manage encryption settings, firewall rules etc) or for data plane operations (such as creating containers, reading and writing data etc). Azure Storage logs in Azure Monitor can be enabled through the Azure Portal, PowerShell, the Azure CLI, and Azure Resource Manager templates.
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