Drive More Simplicity, Efficiency, and Security with ONTAP 9.6

It’s May, which for many families means wrapping up the school year and getting ready for the kids to move up to the next grade—and maybe even a new school—hopefully after a fun summer vacation. For my family, this is especially relevant since my youngest daughter is graduating from high school and heading to college.

 

May is also an important transition time here at NetApp. Every 6 months, like clockwork, we roll out our newest version of NetApp® ONTAP® data management software.

 

Why is this important to you? Well, we understand you’re wrestling with a number of challenges:

• Simplifying infrastructure so an IT generalist can manage it
• Responding to new business opportunities and making sure that your IT is an enabler, not an inhibitor
• Protecting your data from increasing internal and external threats
• Improving application performance (both speed and latency) for your enterprise applications and new workloads like AI
• Getting data to where it’s needed across your hybrid cloud
• Reducing costs (isn’t this on every list of IT challenges?)

Each ONTAP release includes many innovations that help you address these challenges and get the most value out of your data. Our latest version, ONTAP 9.6, delivers significant enhancements in simplicity, operational efficiency, and security for your hybrid cloud.

 

Here’s how the new capabilities of ONTAP 9.6 can help you solve your IT challenges. Do any of these questions resonate with you?

Do you have a staff that is mostly IT generalists?

The improved simplicity in ONTAP and its management dashboard will help your staff quickly identify areas that need attention and then take the right steps to increase the efficiency, availability, and performance of your storage infrastructure. Plus, REST APIs increase automation.

Do you have a hybrid cloud strategy?

ONTAP gives you the flexibility to integrate your on-premises storage with even more clouds for tiering, caching, and data protection. ONTAP 9.6 supports the major public clouds—AWS, Azure, Google, Alibaba, and IBM Cloud.

Do you need to get more value out of your all-flash resources?

With ONTAP powering NetApp AFF all-flash systems, you can free up valuable storage capacity and consolidate even more business-critical workloads by easily identifying cold data and automatically tiering it to the cloud. And you have more cloud options to choose from, to match your cloud partnering strategy.

Do you have a geographically-dispersed workforce that needs to collaborate?

ONTAP gives you more options, now including public cloud, for caching hot datasets close to each of your teams so they get fast response times and can be more productive. Plus, ONTAP delivers even greater security for the cached data by providing encryption while it’s at rest and in transit.

Are you looking for even better performance for your key workloads?

The NVMe/FC capabilities of ONTAP deliver high throughput and consistent low latency for AFF systems—as low as 100µs. That high performance is now available for even more ecosystems, including VMware, Windows, and Oracle Linux in addition to SUSE and Red Hat Linux. Plus, storage path resiliency ensures high availability.

Do you need continuous data availability?

Now, smaller organizations as well as larger businesses with remote locations can deliver cost-effective business continuity in their ONTAP environments with NetApp MetroCluster™ support for entry-class AFF A220 and FAS2750 systems.

Are you concerned with increasing the security of your data?

ONTAP now delivers even stronger security and data protection, with over-the-wire encryption for more environments and encryption key management for multitenant deployments.

 

With the introduction of ONTAP 9.6, which begins shipping in May, you can use ONTAP in more ways, with less effort, and with even better data protection.

A NetApp IT Perspective: Data Science and the Data Lake

As a data driven company, NetApp relies on data science, business intelligence (BI) and analytics to learn, improve, and predict from a myriad of possibilities throughout the enterprise. As I discussed in my earlier blog on organizational structures for data scientists, data analytics is essential to planning and driving business. Data scientists, and their portfolio of methods and tools, are the key to unlocking the most value from our data and improving our odds of success.

Data Warehouse vs. Data Mart vs. Data Lake

Before I jump into describing the symbiotic relationship between data science and data lakes, I want to describe how data can be separated by IT into a data warehouse, a data mart, or a data lake. As an enterprise we need all three. Broadly, a data warehouse is for storing large amounts of data from different sources to feed standard reporting, a data mart is subset of the data warehouse dedicated to a specific organization, and a data lake is for predictive analytics and what-if scenarios.

Our team has spent a lot of time and effort building our data warehouse as a single source of truth. The data is often used for producing standard reports, and if an employee knows the data sources and what answers he or she is seeking, the data warehouse can produce bookings or financial reports. Gartner refers to this as a System of Record, and it’s the traditional way of doing business.

A data mart begins to answer the “How?” and goes beyond the “What?” of business intelligence. It is meant to address departmental needs and provide information regarding how the department is operating based on a subset of data. For example, we have a data mart for sales operations to provide detailed insights from all sales data. It has data that answers questions not available from a simple data warehouse.

From Data Mart to Data Lake (and Data Scientists)

Fast forward about four years from the introduction of the data mart concept to today and our move towards a data lake. A data lake will provide an opportunity to build analytics derived from massive amounts of data and insights beyond straightforward reports or department-specific operational questions. Data lake analytics are predominantly meant for people who are specialized in understanding data models and proficient at working closely with business users to make projections and explore “what-if” scenarios.

The benefits of analyzing a massive data lake cannot be realized by looking at data in a siloed fashion; analysts must understand the process implications in order to come to useful conclusions. For example, data lake analysis cannot be used to make renewal recommendations to the sales team without understanding the sales cycle. A data scientist will spearhead these conversations and requires familiarity with relevant business processes.

In order to prevent misuse of data lake interpretations, we work very closely with each business group on a case by case basis. We must ensure each team has the knowledge and maturity to make decisions based on accurate assessments. It requires time and organizational buy-in to obtain the best return on investment.

The Power of the Data Lake

Our team has been busy building out the data lake technology stack, based on previous experience with a Hadoop-based infrastructure for Active IQ, a system that collects information on customers’ NetApp solutions. Every week the system generates about 100TB of data and 225 million files―and it is growing!

We have taken those lessons learned, and many of the design patterns, to build our data lake architecture. At the same time, we have identified an approach to integrate the data lake into our next generation data center platform as part of efforts to build a cloud aware enterprise.

While the team continues to build out the data lake infrastructure, myself and other team leaders have begun discussions with different business stakeholders to identify data lake use cases. Like a laptop without software, a data lake by itself is of no value unless it can be used to derive predictive analytics with significant business benefit and positive ROI.

A Data Science Center of Excellence

To address the complexities of a massive data lake and build our maturity, we felt it was important to establish a Data Science Center of Excellence. Data scientists can answer questions such as when to connect to the data lake, which models to build out, how to establish visibility across models to prevent duplicate efforts, how to ensure alignment with business processes, and more.