The new challenges of scale: What it takes to go from PB to EB data scale
Big data exploded onto the scene in the mid-2000s and has continued to grow ever since. Today, the data is even bigger, and managing these massive volumes of data presents a new challenge for many organizations.
Big data exploded onto the scene in the mid-2000s and has continued to grow ever since. Today, the data is even bigger, and managing these massive volumes of data presents a new challenge for many organizations. Even if you live and breathe tech every day, it’s difficult to conceptualize how big “big” really is. Going from petabytes (PB) to exabytes (EB) of data is no small feat, requiring significant investments in hardware, software, and human resources.
For instance, an EB is significantly larger than a PB. Much larger. A single EB holds 1,024 PB – enough to hold the entire Library of Congress 3,000 times over, according to Lifewire. On the flip side, a measly PB only has the capacity to hold 11,000 4K movies.
Admittedly, it’s still pretty difficult to visualize this difference. Let’s take it to space. In terms of scale, if a PB is the size of the Earth, an EB would be the size of the sun, according to Backblaze – and, if you recall from science class, it takes about 1.3 million Earths to fill the sun’s volume.
There are those in the marketplace that brag about handling 250 PB of data, but that’s a snowflake in a snowstorm of how truly enormous big data can really be. So, what does it require for organizations to go from PB to EB scale?
1. Start with storage. Before you can even think about analyzing exabytes worth of data, ensure you have the infrastructure to store more than 1000 petabytes! Going from 250 PB to even a single exabyte means multiplying storage capabilities four times. To accomplish this, we will need additional data center space, more storage disks and nodes, the ability for the software to scale to 1000+PB of data, and increased support through additional compute nodes and networking bandwidth. When adding more storage nodes, it is important to ensure that the capacity addition is more optimal and efficient. This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data.
2. Focus on scalability. First and foremost, you need to focus on the scalability of analytics capabilities, while also considering the economics, security, and governance implications. So, how do we achieve scalability? Merely adding more data nodes is insufficient. It is crucial to incorporate both horizontal and vertical scalability, along with a high level of tolerance, resilience, and availability. Simplifying data management and streamlining software administration, including maintenance, upgrades, and availability, have become paramount for a functional and manageable system.
Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth. Leveraging an open-source solution like Apache Ozone, which is specifically designed to handle exabyte-scale data by distributing metadata throughout the entire system, not only facilitates scalability in data management but also ensures resilience and availability at scale.
For instance, one Cloudera manufacturing customer processes 700,000 events each second while another processes five billion messages per day. That’s a huge quantity of data even when compared to other businesses, and this volume will only grow. The global volume of data is expected to swell to 163 zettabytes (ZB) by 2025, 10 times the amount of data existing in the world today. What’s more, it’s estimated that 80% of all that data will be unstructured. We’ll get into that in number four.
3. Examine your tech stack. It’s possible to achieve this scale by cobbling together a number of point solutions, but there is an easier way. When it comes to true economies of scale, a centralized approach to technology via a single platform often outperforms a series of tools.
This is why Cloudera’s single platform solution is so effective. Enterprises can handle much higher data volumes on a unified platform spanning multiple use cases with the scalability to handle the storage and processing of large volumes of data – far beyond petabytes.
And having efficient, maximized use of your data is crucial when it comes to fraud, cybersecurity, applied observability, and intelligent operations (like manufacturing, telco, and utilities). In the case of intelligent operations, real-time data informs immediate operational decisions. An airline carrier needs to know how many gates are open and how many passengers are on each plane – metrics that change from moment to moment. The electric company needs to know how much electricity is flowing through the grid – where there’s too much, and where there’s an outage, instantly.
4. Consider data types. How is it possible to manage the data lifecycle, especially for extremely large volumes of unstructured data? Unlike structured data, which is organized into predefined fields and tables, unstructured data does not have a well-defined schema or structure. This makes it more difficult to search, analyze, and extract insights from unstructured data using traditional database management tools and techniques.
However, with the Cloudera Image Warehouse (CIW), it has become possible to sort and analyze large volumes of unstructured data. Using natural language processing, image recognition, and other advanced techniques, it can extract meaningful insights from unstructured data.
CIW allows you to search for and automatically detect things in images – like stop signs, sidewalks, pedestrians, and weaponry which can be useful for emergency services and law enforcement. And this technology has use for life sciences and manufacturing as well, enabling organizations to gain valuable insights and make more informed decisions.
5. Evaluate data across the full lifecycle. Only 12% of IT decision-makers report that their organizations interact with data across the full analytics lifecycle. Without the full range of analytical capabilities to go from data to insight and value, organizations will lack the capabilities required to drive innovation. Here is how Cloudera visualizes and controls the data lifecycle.
- Ingest: Connect to any data source with any structure across clouds or hybrid environments and deliver anywhere. Process critical business events to any destination in real-time for immediate response.
- Prepare: Orchestrate and automate complex data pipelines with an all-inclusive toolset and a cloud-native service purpose-built for enterprise data engineering teams.
- Analyze: Ingest, explore, find, access, analyze, and visualize data at any scale while delivering quick, easy self-service data analytics at the lowest cost.
- Predict: Accelerate innovation for data science teams, enabling them to collaboratively train, evaluate, publish, and monitor models; build and host custom ML web apps; and deliver more models in less time for business insights and actions.
- Publish: Empower developers to build and deploy scalable, high-performance applications and enable users to create and publish custom dashboards and visual apps in minutes.
We know the global volume of data will only grow larger and more difficult to navigate. But with the right platform, you can handle it all. There’s big data, and then there’s Cloudera.
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