Google handles some of the world’s biggest datasets, and as the creator of Hadoop — which began the Big Data revolution — the tech giant has long been known for its data prowess. So, it’s no surprise that Google Cloud Platform is innovating rapidly with new data services. Here’s a look at the top innovations from Google Cloud in the field of cloud data management.
What Google Cloud data services are about
In a nutshell, Google cloud data services are the services that provide access to Google’s massive collection of cloud tools to manage your data lifecycle end-to-end.
There are two key elements to how data management works in the cloud. First, the cloud as a unified system is made available to customers through a common platform (IaaS), where customer organizations can build their own cloud services on a cloud vendor’s infrastructure. Second, the cloud as a unified system has become available to customers in multiple ways (the “cloud as a service” model), with vendors and service providers adding their customizations to the service. These services come in a wide variety, such as data stores, data flows, and streaming data services.
While organizations start their journey to the cloud by managing their own data in the cloud, eventually, most organizations would leverage serverless and fully managed data tools. This would enable them to avoid the maintenance woes of data infrastructure and just enjoy the benefits of using data. Enabling this and being the go-to place for data management in the cloud is Google Cloud’s goal.
1. Analytics Hub: Share and collaborate around data with external organizations
Analytics Hub is based on BigQuery, the serverless data warehouse service from Google Cloud. Analytics Hub is out to solve challenges related to organizations sharing their data with other organizations. It puts organizations in the driving seat of their data, giving them full control over who accesses their data.
At the heart of Analytics Hub are datasets and exchanges. Datasets are simply views of any data that you’d like to share with any external organization. Exchanges are the place where these external organizations can view and subscribe to your datasets. In terms of pricing, data subscribers pay for the queries they run on any data they access, and as the owner of the data, your organization pays for the storage of that data.
There are different types of datasets, such as public, commercial, and internal datasets. Public datasets are made available by Google and include data about weather, COVID-19, and more. Commercial datasets are made available by data vendors. Internal datasets are ones that your organization creates to share with internal teams or external partners and vendors.
What Analytics Hub brings to the table is a robust way to manage your data in the cloud without the hassle. How an organization shares its data externally is central to how it functions and does business. In that sense, Analytics Hub is critical in a world where organizations no longer operate as lone islands but integrate freely with other organizations. This trend is set to grow in the years ahead, and Analytics Hub will see growing usage.
2. Dataplex: Manage multiple data lakes and data warehouses from one place
Dataplex is an intelligent data fabric that provides a way to centrally manage, monitor, and govern your data. It is a set of building blocks for constructing a data pipeline. Dataplex gives you the flexibility to choose where you’d like to store your data and manage them in a unified way.
Previously, an organization would have various silos of data strewn across a datacenter and various cloud locations. These silos would be used by different teams in different ways. All this breeds confusion and bottlenecks in the org-wide data workflow. With Dataplex, organizations can build a common data fabric that spans all their data stores, no matter where they are stored.
The potential of an organization deploying Dataplex is to develop a fully integrated pipeline where you manage data in multiple data lakes and data warehouses. Whether these data lakes are managed in Google Cloud or by another data vendor, they can be unified with Dataplex.
When managing data in different locations, it is important to enforce consistent controls across your data to ensure unified security. Dataplex enables this using a set of policies that can be applied across some or all of your data. This brings strong governance and compliance capabilities.
With Dataplex, you can give teams access to data no matter where the data lives. A significant feature of Dataplex is its one-click analytics environments. Think of these as data templates that can be consumed ready-made by various teams. The templates can be customized for each team or product. Data scientists and analysts can become more productive as they get easier access to data. In addition, data owners have peace of mind knowing exactly who uses their data and how they access it.
3. Datastream: Serverless data integration service
Datastream is a serverless data change capture service. It not only captures changes to data in your file system but allows you to integrate this data with other datasets on the fly. It can update existing data and create new data that is ready for consumption by data teams. Data can be fed into Datastream from a streaming data service, and this data can be synchronized with low latency. Being a serverless solution, Datastream takes the infrastructure management out of the equation, allowing you to focus on just how you’d like to use your data.
The key use case for Datastream is to integrate data across different databases and applications. Typically, data integration takes a lot of time and involves writing custom plugins and integrations each time. With Datastream, this process can be greatly quickened. Just point your data stream to Datastream, and you have a bunch of controls to sync that data in near real-time. This makes the data very usable and useful for analytics and data science teams.
Quickening data workflows is a big priority for organizations today. Datastream is a vital part of doing this in the Google Cloud ecosystem.
4. Dataflow Prime: Built for Big Data processing
Dataflow Prime enables organizations to move beyond relational data and to leverage the power of the cloud to gain insights from Big Data analytics. It is based on Apache Beam and excels at real-time data analysis of streaming data. It has great native support for the most popular AI and ML frameworks and programming languages like Tensorflow and Python.
Dataflow has a wide range of use cases such as anomaly detection, predictive analytics, and IoT sensor data processing. For collaboration, Dataflow has a feature called pipelines that allows teams to share workflows. There is also support for shared ML notebooks via Google’s Vertex AI.
Dataflow is well integrated with BigQuery and the rest of Google Cloud’s data services. It is key to any organization’s data strategy within Google Cloud.
Stay on top of Big Data trends with Google cloud data services
It’s also true that many of the new offerings are being built on the lessons learned from other services or areas of expertise. Data is in the spotlight and likely will be for the foreseeable future. By its nature, cloud services are changing fast. Building a portfolio of managed cloud data services is key to staying on top of the Big Data trends and the evolving IT landscape.
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