Cloud Data Lakes

Cloud Data Lakes – Create a multi-cloud data lake using Terraform and run an Apache Spark data pipeline on COVID-19 data.

When I started working on large enterprise data platforms five years ago, the common data lake architecture had to go with a public cloud provider or on-premises platform. These data lakes are rapidly growing from several terabytes to petabytes of structured and unstructured data (only 1% of unstructured data is analyzed or used at all). While traditional data lakes face capacity issues, a cloud implementation is known as vendor lock-in.

Cloud Data Lakes

Cloud Data Lakes

Today, a hybrid multi-cloud architecture that uses two or more public cloud providers is the preferred strategy. 81% of public cloud users report using two or more cloud providers.

Trends Driving Growth In Cloud Data Lakes L Sisense

Cloud providers offer a variety of tools and services to move data to the cloud and transform data. In this article, we will create a cloud-native data pipeline using Apache Spark.

We will create a Spark ETL Job in Google Cloud Dataproc that will load ECDC Covid19 data from Azure Blob storage, transform it and then deploy it to 3 different cloud stores: Amazon Redshift, Azure SQL and Google BigQuery.

Here we automate the provisioning of cloud infrastructure using Infrastructure as Code (IaC) with Terraform. IaC clusters allow you to easily spin up and shut down, so you can keep the cluster running while it’s running.

This application container can be built and run on AWS/Azure/GCP resources compatible with the free tier. Sign up for free credits to Azure/GCP and AWS public cloud services. Follow the links below to do each of them.

What Is A Data Lake?

After you log in to the GCP console. Activate Cloud Shell by clicking on the icon shown in the screenshot below. Cloud Shell provides command-line access to a virtual machine instance, and this is where we’ll set up our use case. (Alternatively, you can do this on a laptop terminal as well)

We’ll use the service’s basic authentication method because it can be used to automate the entire process (think CICD).

Go to the Azure Active Directory overview within the Azure portal – then select the Application Registration tab. Click the New Registration button above to add a new application to Azure Active Directory. On this page, set the following values, then click Create:

Cloud Data Lakes

Once Azure Active Directory is available, we can create a Client Secret that can be used for authentication – these are optional certificates and secrets. This screen displays the certificates and client secrets (ie passwords) associated with this Azure Active Directory Application.

Data Lake Vs. Data Warehouse: What Are The Differences?

Click on the “New Customer Secret” button, then enter a short description, select a term and click on “Add”. Once the client password is generated, it will be displayed on the screen –

So copy it now (or you’ll have to update). This is the customer_secret you need.

You must assign a role to the app to access your subscription’s resources. Here we will give you a paper on subscription limits.

E) e. Select Save to set the role. You see your application in the list of users with roles in that domain.

Sap Data Warehouse Cloud Integrated With Sap Hana Data Lake

In the Google Cloud Shell terminal, run the following commands to set the environment variables for Azure auth

Follow the AWS documentation to create a user with Programmatic access and Administrator permissions by adding it directly to the AdministratorAccess policy. *

When you create a user, you will receive a login ID and a secret login key. Create new environment variables as below and run in Google Terminal Shell

Cloud Data Lakes

Finally, we have finished installing the infrastructure. We create the necessary resources on GCP, AWS and Azure.

Infographic: 5 Reasons To Dive Into Data Lakes

Here, we created a hybrid cloud infrastructure and used Apache Spark to read and write real-time Covid19 databases across three different cloud storage locations.

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A data lake is an advanced data processing platform that supports more comprehensive data and analytical processing than common SQL databases.

For more than a decade, enterprises have invested heavily in building their primary data lakes. However, in the last few years, a new trend has emerged, the Cloud Data Lake.

What Are Cloud Data Warehouses?

Cloud Data Lake is a next-generation cloud-based Data Lake that provides more attractive price/performance, a variety of analytics engines, best-of-breed tools, and almost all of it in Cloud storage.

Living in a public cloud environment like AWS and Microsoft Azure, Cloud Data Lake is more than just storage. Cloud Data Lake is a complete analytics environment supporting a variety of analytics tools and languages ​​(SQL, R, Python, Java, Scala, etc.) supporting a variety of workloads from traditional analytics, BI, streaming event/IoT. for processing, Machine Learning and AI processing.

Compared to their on-premises counterparts, cloud data lakes bring distinct advantages in terms of storage, computing, and cost. However, with these advantages come new challenges regarding the capabilities required and the operational complexity of Cloud Data Lakes. This post will explore the benefits and challenges of this new analytics platform.

Cloud Data Lakes

One of the main challenges of deploying a data lake is data growth. Information is produced at astonishing rates and accumulated rapidly.

Data Lake As A Service: Simpler, Faster Benefits

With an in-house data lake, you need to regularly monitor the progress of the data in your data lake. As your data grows and approaches capacity, you may need to add additional hard drives to existing hardware or purchase additional compute and storage to expand your cluster, even if you don’t need additional power.

With Cloud Data Lakes, the storage is unlimited (serverless) with a cloud vendor’s low-cost object storage, such as S3 on AWS and Azure Data Lake Storage (ADLS) on Azure. These storage layers offer 9x stability and availability and have automatic geo-augmentation. No capacity plan required for data growth with unlimited capacity.

Decoupling storage from compute is important because it increases the flexibility and capacity of the Cloud Data Lake.

In Cloud Data Lake, computing, analytics engines such as Spark, Hive, Presto or Impala can be on-demand and computing elastic.

Managed Data Lake Creation

Analytics engines can be customized for specific purposes. Different teams can perform ETL, ML, ad analytics, etc. on the same shared data. They can spin up different compute engines for different workloads, such as It’s common to spin up a Spark cluster for several hours to shut down compute resources after the pipeline is finished.

As a result, you can provide optimized infrastructure for specific workloads, and since the infrastructure can be temporary, the result is always lower infrastructure costs.

Analytics engines can be configured to scale computing on demand, adding elastic computing power to your data lake. This often results in higher performance SLAs with lower infrastructure costs in the long run.

Cloud Data Lakes

With sets, these scenarios are almost impossible. Add additional hardware to increase the computing power of your data lake. Unless you’re lying around, this means removing and installing the device; this process usually takes several weeks to several months. To avoid or at least reduce this delay, you need to constantly analyze your computing power and predictions to stay ahead of the game.

Data Lake. Structured And Unstructured Data. Vector Concept Illustration Stock Vector

With Cloud Data Lake, you only pay for the computing you use, and when you’re not using it, you can easily turn it off and avoid unnecessary costs.

For on-premise data lakes, once you’ve invested in new equipment, you’ll own it. Another cost if not used – you could be stuck for 3-5 years. This means that while there are new options that better suit your workloads, you can only use them during hardware upgrades.

Software licensing costs are the same. With in-house data lakes, you’ll need to purchase software licenses and software support agreements, and if you find you’re not using the software, it doesn’t matter, you’re usually not getting your money’s worth. With Cloud Data Lakes, software and services are typically billed by the hour—if you’re not using the service, you don’t have to pay for it.

Enterprises should evaluate the use of Cloud Data Lakes based on the architectural advantages described above. However, Cloud Data Lakes present new challenges in terms of complexity and operational expertise requirements.

How Our Threat Analytics Multi Region Data Lake On Aws Stores More, Slashes Costs

Common challenges for cloud data lakes include long production cycles, integration issues with legacy applications and users, ensuring security and compliance, managing data and managing ongoing costs.

For example, around security and compliance, you need to think carefully about protecting your data, especially if you plan to store sensitive data in a cloud data lake. As with the on-premises solution, you must define data encryption at rest as well. Also, don’t share your data and internet services. This means no public IP address

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