Choosing how and where to store unstructured data is a big decision for any enterprise. From compliance questions to calculating the total cost for managing and maintaining a new data storage solution, app modernization can bring up many unknowns, causing organizations to weigh their options in creating their on-premise solution or integrating a third party.
Luckily, many open-source tools help organizations arrive at a solution and create a cloud environment suited to their needs. Our experts are weighing in about the process of modernizing data lakes and data warehouses with Google Cloud Platform and how enterprises can quell governance, compliance, and bandwidth concerns with a Google Cloud Storage solution for their most extensive sets of unstructured data.
What is a Data Lake?
To make an informed decision about whether or not your organization could benefit from Google Cloud Platform data ingestion and creating a Google Cloud Storage data lake, let’s first understand what a data lake is. Data lakes are created when a steady stream of data flows into one centralized location. They differ slightly from a data warehouse in that the data has not been transformed and is unstructured.
Unlike structured data, a data lake is a general repository of unstructured data that can later be structured or categorized and used in data analytics and reporting. Organizations use data lakes to dump as much data as possible and then move data into its category for application processing. Then, use the data for machine learning, data warehousing, reporting, analytics, and other applications.
What is Google Cloud Storage?
Modernizing data lakes and data warehouses requires choosing the right third-party platform or on-premise platform for the job. And while there are certainly many benefits that come from hosting and storing data in a legacy system or on-site, there are even more advantages with storing big data in a third-party solution like Google Cloud.
Google Cloud Storage is a public cloud storage system built for housing and storing large sets of unstructured data. In addition to ease of access, teams that store extensive data in Google Cloud Storage also alleviate potential compliance and governance issues while gaining all of the benefits of Google Cloud data privacy and security standards.
As a reservoir for data, Google Cloud storage offers virtually unlimited capacity for any kind of data. Application developers can create “buckets” where data will be stored. Data can be categorized, separated by project, secured, and moved when necessary. Storage in the cloud can be unstructured or structured and used as the back end for public or private applications.
Advantages of Storing Big Data in Google Cloud Platform
One of the key components in creating a data lake is creating a central storage location. Many open-source tools online can act as central storage to your data lake, but Google Cloud Storage offers secure and cost-effective solutions for storing big data.
Instead of building in-house infrastructure to support big data, the cloud offers an easier way to scale for additional storage capacity and support for newer technology. Big data is unstructured and requires truly scalable storage resources. Google Cloud Storage will scale up or down as necessary to support big data.
Because it’s the cloud, data is always available. Real-time analytics and reporting require constant storage connectivity. Any outages could affect output and analytic functionality. With Google Cloud Storage, the data is always available, and failover storage can be used to cover the low chance of cloud disruption.
One of the most significant advantages was the cost. Smaller organizations do not have the budget for high-end house technology. Google Cloud Platform provides storage and affordable ways to leverage the latest technology that would otherwise be unavailable due to building and maintaining infrastructure. In addition, security tools are readily available, and organizations can scale ample data storage at a fraction of the cost. Availability is also higher for remote workers since all data is located in the cloud.
Creating a Google Cloud Platform Data Lake
While the guidance for creating a Google Cloud Platform data warehouse differs slightly from creating a Google Cloud Platform data lake, the open-source tools available within the Google Cloud Platform host all of the capabilities necessary for building whatever data repository your organization needs and can help organizations get over the hurdles of scalability, governance, and analytics management that can make on-premise tools an inhospitable environment for your most important data.
Timeline & Priorities
To create a Google Cloud Storage data lake or move an existing data lake to the Cloud, it’s always best to create a timeline and list priorities for the effort. A plan is necessary to ensure that data can be moved to the cloud smoothly. The plan should include the data stored in the cloud, the security to protect it, and the applications and users accessing the data.
Timelines depend on the amount of data and the project plan. Data is often the last component to migrate, but sample data is often moved during tests to ensure that applications function when all data is migrated over to the cloud. Migrations often happen slowly and during off-peak hours so that productivity is not affected.
Choosing the Right Tool for the Workload
Modernizing data lakes and data warehouses with Google Cloud Platform requires teams to know workload patterns and profiles. The type of workload will determine the kind of cluster you should run to handle the different layers of your Google Cloud Platform data lake. In addition, Google has several applications to help migrate data, maintain it, secure it, and create archives and backups.
For big data, the BigQuery Data Transfer Service will move a few gigabytes of data or terabytes if necessary. This tool will let organizations move only the data they want without migrating unused files that waste resources. In addition, it can be scheduled so that information is synchronized in the future between on-premise infrastructure and the cloud.
Smaller volumes of data can be transferred using the gsutil command-line utility. Administrators can move data using the gsutil utility on the fly or when some scheduled data did not copy over to the cloud. It’s mainly used when fewer than a few terabytes must be migrated over to cloud storage manually.
Using Google Cloud Platform’s Separate Storage and Compute
BigQuery is a serverless model that allows administrators to migrate and manage data without the expense and overhead of virtual machine instances. It can be used to schedule batch jobs so that the organization pays per project, reducing costs of bandwidth and resources. The storage silos used in Google Cloud are separated from the computing power used in BigQuery migrations. As a result, the organization can run multiple data migration projects that move data from one location to cloud storage without affecting applications or individual projects.
Modernize Data Operations
After your data has been prepared for the migration, it’s also essential to optimize deployment operations by pooling clusters and rewriting deployments as code. This will help with platform rendering as you create the Google Cloud Storage data lake and organize big data. In addition, the serverless nature of BigQuery execution and project migration reduces the overhead on computing power. It gives administrators the ability to write code without worrying about server costs and configurations.
Governing Your Google Cloud Platform Data Lake
Once existing workloads and applications have been migrated to the Cloud, teams benefit almost immediately from Google Cloud Platform data ingestion and the suite of analytics and management tools at their disposal for interpreting and synthesizing raw data.
Dataproc will process, query, stream, and output to machine learning applications for organizations using Hadoop and Spark. Dataproc can be used to create clusters and automate data migration dynamically. Automation will take over any synchronization maintenance, freeing up administrators to focus on other projects.
Cloud Data Fusion
You need a pipeline to move data to the cloud and work with them within an application. Cloud Data Fusion can build these pipelines using either the Google Cloud console or a UI like Pipeline Studio or Wrangler. Pipelines built with Cloud Data Fusion will transform, clean, and transfer data to be ready for integration into your applications.
With your big data stored in the cloud, you can now use it for real-time analytics. Google Smart Analytics is a platform that will give you actionable insights into your data. It can help drive future revenue initiatives and provide direction on new products and services. It integrates directly with BigQuery and your cloud data to gain insights into how business is thriving and changes that can make it even more productive.