As organizations expand their use of cloud computing services, more of their sensitive data inevitably moves to and lives in the cloud. Much of this sensitive data is unstructured and can be challenging to secure. Despite this potential challenge, the usefulness of cloud for data storage and processing is too big for most organizations to ignore and has in turn led to data sprawl, where their sensitive data is spread over many resources, both in the cloud and on-premise. Addressing data sprawl requires solutions that can discover, manage, and secure sensitive data, especially unstructured data, as it spreads.

To help organizations confidently move their sensitive data to the cloud, Google Cloud works diligently to earn and maintain customer trust. Control and transparency are pillars of our approach to offering a trusted cloud. Therefore, we’ve been expanding our capabilities to act on unstructured data as sprawl increases.

Given the importance of these capabilities to our strategy, we are happy to announce today that Forrester Research has named Google Cloud a Leader in The Forrester Wave™: Unstructured Data Security Platforms, Q2 2021 report, and rated Google Cloud highest in the current offering category among the providers evaluated.

The report evaluates the 11 most significant providers with platform solutions to secure and protect unstructured data, spanning from cloud providers to data security-focused vendors. The report notes that “Google offers breadth and depth with built-in data security in the cloud. Google Cloud Platform, Google Workspace, and BeyondCorp Enterprise have underlying data security products and features for protecting customer data.”

Google Cloud tools focused on protecting unstructured data were developed and battle-tested internally at Google to alleviate some of our own data security challenges. This brings the best of Google security to the organizations utilizing Google Cloud and our security tools. The report highlights that “Google productizes capabilities originally developed to secure its own business, and brings a disciplined approach to product enhancements for enterprise requirements. It serves a wide range of enterprise and mid-market, with a focus on emphasizing data protection needs by industry. ”

Google Cloud’s data security strategy focuses on meeting customers wherever they are in their cloud migration journey. The report highlights that “Google further enables a Zero Trust approach with third-party integrations through its BeyondCorp Alliance of partners in device management, endpoint security and gateways.”

Google Cloud received the highest possible score in sixteen criteria, in total receiving the most 5 out of 5 ratings among all vendors assessed. These criteria include: Data Intelligence, Access Control, Deletion, Obfuscation-Scope, Obfuscation-Key Management, Deployment, Security and Risk, APIs and Integration, Data Security Platform Vision, Data Security Execution Roadmap, Performance, Planned Enhancements, Zero Trust Enabling Partner Ecosystem, Diversity, Equity and Inclusion, Installed Base, and Revenue. 

Notably, Google Cloud received the highest possible score in the Obfuscation criteria. Obfuscation can help protect sensitive data, like personally identifiable information (PII), which is critical to many enterprise workflows. Cloud DLP helps customers inspect and mask this sensitive data with techniques like redaction, bucketing, and tokenization, which help strike the balance between risk and utility. This is especially crucial when dealing with unstructured or free-text workloads, in which it can be challenging to know what data to redact. More than 150 detectors combine to power Cloud DLP’s masking, which can be deployed in data migrations and business workloads like real-time data collection and processing. For Obfuscation specifically, the report mentioned that Google “takes a broad view of DLP, which includes in-line redaction of sensitive elements in unstructured data and DLP APIs that extend support to additional data types like images or other media.”

We are honored to be a Leader in The Forrester Wave™ Unstructured Data Security Platforms Q2 2021 report, and look forward to continuing to innovate and partner with you on ways to make your digital transformation journey safer as we work to become your most trusted Cloud.

A copy of the full report can be viewed here.

If your organization is one of the 52% of SAP customers whose top analytics pain point is data integration¹, Google Cloud has got you covered. By working with partners like Qlik, we are expanding our integration options and bringing real-time replication capability for SAP to BigQuery.

Integrated data for accelerated insights

BigQuery—our fully managed, enterprise data warehouse that can scale up to petabytes on demand and execute queries in seconds—allows SAP customers to consolidate enterprise data silos and confidently derive more use and value from their data.  Customers can accelerate and simplify the delivery of SAP data on BigQuery with the latest Qlik Data Integration platform offering for Google Cloud which allows data integration using an automated, near real-time data pipeline through Qlik Replicate, whether data originates from legacy SAP environments, SAP HANA, or SAP application servers. Additionally, Qlik Compose for Data Warehouses can be used to easily generate and automate logical data models from SAP directly in BigQuery freeing up more time for data analysts to leverage advanced built-in capabilities such as BigQuery ML to derive greater value and insights using standard SQL without the need for advanced programming expertise.

Delivering faster results with real-time 

Traditional extract-transform-load (ETL) solutions operate on a batch basis, pulling data sets from SAP daily, hourly, or every minute. These tools often require manual mapping of multiple data fields so that data flows accurately. Given SAP’s highly complex table relationships, in which a single transaction can result in multiple changes, this can be a time consuming and tedious process. ETL can also increase the burden on your SAP systems.

Qlik Replicate simplifies this with its intuitive user interface where you can set up real-time data replication between SAP and BigQuery, eliminating the need for manual coding. And, to prevent system overhead, as soon as a new transaction is entered into SAP, the resulting data is replicated into BigQuery in a process known as change data capture (CDC) from SAP’s log layer. This means that data transfer can benefit from high performance with minimal impact on the source system’s resources. Read our latest white paper to learn how to extract SAP data into BigQuery leveraging Qlik Replicate. 

Solution expertise for all core SAP workloads

It doesn’t matter what database your SAP system runs on, Qlik Replicate supports all core SAP systems. It automates real-time data replication and decodes SAP’s complex, application-specific data structures into formats that flow smoothly into BigQuery. This ensures that anyone who depends on data analytics has the most current and relevant SAP data they need. For its robust solution capabilities Qlik has received a new “SAP on Google Cloud Expertise” designation for supporting:

• Fast onboarding and accelerated replication of SAP data into Google Cloud
• Real-time and continuous data replication from SAP applications to BigQuery
• Support for all core SAP modules and a broad set of data sources
• Automated data integration, which cuts resource requirements for initial delivery and ongoing maintenance

Proven customer results

Many SAP customers have experienced the benefits of leveraging Qlik alongside BigQuery for data analytics and AI at scale, including German luxury department store chain Breuninger. In order to meet its customers’ growing and changing expectations,Breuninger needed to accelerate its time to insight from data sources across a highly dispersed landscape of on-premises databases and systems, including SAP. The company uses Qlik Replicate to feed corporate data from modules in its SAP system into BigQuery and integrate its varying on-premises databases with the Google Cloud environment. This has yielded game-changing, real-time customer insights for the retailer.

What could your business do with faster, more integrated insights? Learn more about BigQuery for SAP customers and also how Qlik and Google Cloud can help you modernize and automate data integration and analytics.

Editor’s note: Manhattan Associates provides transformative, modern supply chain and omnichannel commerce solutions. It enhanced the scalability, availability, and reliability of its software-as-a-service through a seamless migration to Google Cloud SQL for MySQL.

Geopolitical shifts and global pandemics have made the global supply chain increasingly unpredictable and complex.

At Manhattan Associates, we help many of the world’s leading organizations navigate that complexity through industry-leading supply chain commerce solutions like warehouse management, transportation management, order management, point of sale and much more, to continuously exceed increasing expectations.

The foundation for those solutions is Manhattan Active® Platform, a cloud-native, API-first microservices technology platform that’s been engineered to handle the most complex supply chain networks in the world and designed to never feel like it.

Manhattan Active solutions enable our clients to deliver exceptional shopping experiences in the store, online, and everywhere in between. They unify warehouse, automation, labor and transportation activities, bolster resilience, and seamlessly support growing sustainability requirements.

More Resiliency and Less Downtime

Manhattan Active solutions run 24×7 and need a database solution that can support this. Cloud SQL for MySQL helps us meet our availability goals with automatic failovers, automatic backups, point-in-time recovery, binary log management, and more. Cloud SQL also allows us to create in-region and cross-region replicas efficiently with near zero replication lags. We can create a new replica for a TB size DB in under 30 minutes, a process which used to take several days.

We provide a 99.9% overall up-time service level agreement (SLA) for Manhattan Active Platform, and Cloud SQL helps us keep that promise. Unplanned downtime is 83% less than it would have been with our previous database solutions.

Flexibility and Total Cost of Ownership

One of the fundamental requirements in a cloud-native platform like Manhattan Active is a robust, efficient, and cost-effective database. Our original database solutions struggled across different cloud platforms and created challenges in total cost of ownership and licensing.

We needed a more cost-efficient approach to managing a highly reliable and available database engine that could operate as a managed service, and Cloud SQL delivered.

We were able to move every Manhattan Active solution from our previous cloud vendor to Google Cloud, including the shift to Cloud SQL, with less than four hours of downtime.

Today, we run hundreds of Cloud SQL instances and operate most of them with just a few database administrators (DBA). By offloading the majority of our database management tasks to Cloud SQL, we significantly reduced the cost to maintain Manhattan Active Platform databases.

We also need a solution where we resize our database within minutes. This requirement is needed to manage database performance and infrastructure costs. The ease of resizing our database within minutes allows us to keep the optimal performance levels and saves significantly on overall infrastructure costs.

A Winning Innovation Combination

Cloud SQL provides highly scalable, available, and reliable database capabilities within Manhattan Active Platform, which helps us provide significantly better outcomes for our clients and better experiences for their customers.

Learn more about how you can use Cloud SQL at your organization. Get started today.

At Redisconf 2018 in San Francisco last month, we announced the public beta of Cloud Memorystore for Redis, a fully-managed in-memory data store service. Today, the public beta is available for everyone to try. Cloud Memorystore provides a scalable, more secure and highly available Redis service fully managed by Google. It’s fully compatible with open source Redis, letting you migrate your applications to Google Cloud Platform (GCP) with zero code changes.

As more and more applications need to process data in real-time, you may want a caching layer in your infrastructure to reduce latency for your applications. Redis delivers fast in-memory caching, support for powerful data structures and features like persistence, replication and pub-sub. For example, data structures like sorted sets make it easy to maintain counters and are widely used to implement gaming leaderboards. Whether it’s simple session caching, developing games played by millions of users or building fast analytical pipelines, developers want to leverage the power of Redis without having to worry about VMs, patches, upgrades, firewall rules, etc.

Early adopters of Cloud Memorystore have been using the service for the last few months and they are thrilled with the service.

Simple and flexible provisioning
How you choose to deploy Cloud Memorystore for Redis depends on the availability and performance needs of your application. You can deploy Redis as a standalone instance or with a replica to provide high availability. But while replicating a Redis instance provides only data redundancy, you still need to do the heavy lifting of health checking, electing of a primary, client connections on failover, etc. The Cloud Memorystore service takes away all this complexity and makes it easy for you deploy a Redis instance that meets your application’s needs.

Cloud Memorystore provides two tiers of service, Basic and Standard, each with different availability characteristics. Regardless of the tier of service, you can provision a Redis instance as small as 1 GB up to 300 GB. With network throughput up to 12 Gbps, Cloud Memorystore supports applications with very high bandwidth needs.

Here is a summary of the capabilities of each tier:

Provisioning a Cloud Memorystore instance is simple: just choose a tier, the size you need to support the instance availability and performance needs, and the region. Your Redis instance will be up and running within a few minutes.

“Lift and shift” applications
Once provisioned, using Cloud Memorystore is a breeze. You can connect to the Redis instance using any of the tools and libraries you commonly use in your environment. Cloud Memorystore clients makes use of IP addresses to connect to the instance. Applications always connect to one IP address and Cloud Memorystore ensures the traffic is directed to the primary in case there is a failover.

Other key features
Whether it’s provisioning, monitoring or scaling memory, Cloud Memorystore simplifies common management tasks.

Security
Open-source Redis has very minimal security, and as a developer or administrator, it can be challenging to ensure all Redis instances in your organization are protected. With Cloud Memorystore, Redis instances are deployed using a private IP address, which prevents the instance from being accessed from the internet. You can also use Cloud Identity & Access Management (IAM) roles to ensure granular access for managing the instance. Additionally, authorized networks ensure that the Redis instance is accessible only when connected to the authorized VPC network.

Stackdriver integration
Cloud Memorystore instances publish all the key metrics into Stackdriver, Google Cloud’s monitoring and management suite. You can monitor all of your instances from the Stackdriver dashboard, and use Stackdriver Logging to get more insights about the Redis instances

Seamless memory scaling
When a mobile application goes viral, it may be necessary to provision a larger Redis instance to meet latency and throughput needs. With Cloud Memorystore you can scale up the instance with a few clicks, and the Standard High Availability tier lets you scale the instance with minimal disruption to the application.

On-demand pricing
Cloud Memorystore provides on-demand pricing with no upfront cost and has per second billing. Moreover, there is no charge for network traffic coming in and out of a Cloud Memorystore instance. For more information, refer to Cloud Memorystore pricing.

Coming soon to Cloud Memorystore
This Cloud Memorystore public beta release is just a starting point for us. Here is a preview of some of the features that are coming soon.

• New region support
• Import-Export of RDB files into a Google Cloud Storage bucket
• Support for Redis 4.0
• Support for Redis Cluster

We are excited about what is upcoming for Cloud Memorystore and we would love to hear your feedback! If you have any requests or suggestions, please let us know through Issue Tracker. You can also join the conversation at Cloud Memorystore discussion group.

Sign up for a $300 credit to try Cloud Memorystore and the rest of GCP. Start with a small Redis instance for testing and development, and then when you’re ready, scale up to serve performance-intensive applications.

Want to learn more? Register for the upcoming webinar on Tuesday, June 26th 9:00 am PT to hear all about Cloud Memorystore for Redis.

Enterprises expect extreme reliability of the database infrastructure that’s accessed by their applications. Despite your best intentions and careful engineering, database errors happen, whether that’s machine crashes or network partitioning. Good planning can help you stay ahead of problems and recover more quickly when issues do occur.

This blog shows one approach of deploying a database architecture that implements high availability and disaster recovery for MySQL on Compute Engine, using regional disks as well as load balancers.

Any database architecture must provide approaches to tolerate errors and recover from those errors quickly without losing data. These approaches are expressed in RTO (recovery time objective) and RPO (recovery point objective), which offer ways to set and then measure how long a service can be unavailable, and how far back data should be saved.

After a database error, a database must recover as fast as possible with an RTO as small as possible, ideally in seconds. There must be as little data loss as possible—ideally, none at all. The desired RPO is the last consistent database state.

From a database architecture and deployment viewpoint, this can be accomplished with two distinct concepts: high availability and disaster recovery. Use both at the same time in order to achieve an architecture that’s prepared for the widest range of errors or incidents.

Creating a resilient database architecture

A high-availability database architecture has database instances in two or more zones. If a server on a zone fails, or the zone becomes inaccessible, the instances in other zones are available to continue the processing. The figure below shows two instances, one in zone zn1, and one in zone zn2. The load balancer in front supports directing traffic to a healthy database instance available for read and write queries.

A disaster recovery architecture adds a second high-availability database setup in a second region. If one of the regions becomes inaccessible or fails, the other region takes over. The figure below shows two regions, primary and DR. Data is replicated from the primary to the DR region so that the DR region can take over from the latest consistent database state. The load balancer in front of the regions directs traffic to the region in charge of the read and write traffic. Here’s how this architecture looks:

In addition to the database instance setup, a regional disk is deployed so that data is written simultaneously in two zones, proving fail-safe in the event of zone failure. This is a huge advantage of Google Cloud, allowing you to skip MySQL-level replication within a region. Each write operation to disk is done in two zones synchronously. When the primary instance fails, a standby instance is mounted with regional persistent disk(s), and the database service (MySQL) is then started using the same. This brings the peace of mind of not worrying about replication lag or database state for high availability.

From a disaster recovery process view, the following happens over time during a failure situation:

• Normal steady state database operation
• A failure happens and a region becomes unavailable or the database instance inaccessible
• A decision must be made to fail over or not (in case there is the expectation that the region becomes available soon enough or the instance becomes responsive again)
• DNS is updated manually, therefore it redirects application traffic to a second region
• Fallback to the primary region after it becomes available again is optional, as the second region is a fully built-out deployment

From a high-availability process view, the following happens over time during a failure situation:

• Normal steady state database operation
• Database instance fails or becomes unavailable
• Launch the standby instance
• Mount regional SSD and start database
• Automatic redirection of application traffic to the standby via load balancer
• After the failed or unavailable instance becomes available again, a fallback can take place or not

The database architecture shown demonstrates a highly available architecture supporting disaster recovery. With regional disks and load balancers, it is straightforward to provide a resilient database deployment.

Find out more about load balancers and regional disks. Check out general HA and DR processes and detailed steps in the initial part of the reference guide. Try it out to become familiar with the architecture as well as the two major failover processes.