November 30, 2020 By Holly Vatter 3 min read

Big data will continue to grow at a rapid pace this year and beyond, supporting current and future artificial intelligence (AI) and Internet of Things (IoT) initiatives.

“Newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes). [By 2018] we were already at 33 zettabytes, leading IDC to predict that in 2025, 175 zettabytes (175 trillion gigabytes) of new data will be created around the world.”[1]

There are many available platforms for storing, managing and exploring big data—along with many deployment options including public and private cloud, hybrid, multiregional, and multicloud. Most organizations will adopt a distributed environment and need the ability to quickly and safely migrate data between geographies, platforms and deployment options while managing the complexity of growing data volumes and new types of semi-structured and unstructured data.

Migrating data from ground to cloud with scalability, immediacy and no downtime

The focus of data complexity management has shifted from solely managing, storing and exploring Hadoop big data on premises, to adopting flexible and competitive cloud offers. Cloud deployments offer several key advantages including the ability to adjust the environment on demand. In addition, today’s cloud data lakes are often part of by a more mature technology landscape that supports the full data journey, from source to target, including data integration, transformation, aggregation, and BI and visualization.

It’s also worth noting that cloud data lakes are often better suited to the complex deep learning required for artificial intelligence and machine learning applications. This is due to the availability of new cloud-native tools designed for the complexity of modern data and the ability to adopt cloud “as a service.” Cloud services are typically easier to deploy, more intuitive to use, and quicker to access for data scientists and analysts who need to spin up an environment for a new project.

When migrating data from on premises, across geographies, data platform architectures, or between cloud storage providers, there are several characteristics you should look for to help meet new boardroom priorities, realize new business opportunities, and ensure data integrity. They are:

  • Scaleable. The key factor driving organizations to adopt cloud platforms is the ability to scale their environments on demand, including to terabyte and exabyte scale. As your data estate grows, migrating data between your on premises and cloud data platforms will grow in importance.
  • Immediate. Administrators should be empowered to easily deploy the solution and begin migration of data lake content to the cloud immediately. This solution should be non-intrusive—requiring no changes to applications, cluster or node configuration, or operations—saving your best IT people to be strategically focused. A quick and seamless migration increases business agility, delivers up-to-date business-critical data, and helps the organization to keep ahead and realize new business opportunities.
  • Live. The chosen technology needs to support complete and continuous migration of distributed data without requiring any production system downtime or business disruption. This should be true even in demanding situations such as disaster recovery or when the source data is under active change. One small outage caused by an unreliable method for data migration can lead to disruption or downtime resulting in the loss of customer confidence, damage to the organization’s reputation, and even financial repercussions. Traditional approaches to large-scale Hadoop data migration rely on repeated iterations where source data is copied, but they do not take ongoing changes into account during that time. They require significant up-front planning and impose operation downtime if there is a need to ensure data are migrated completely.

Big Replicate LiveData Migrator is automated and scalable for continuously available data

Keeping data consistent in a distributed environment — whether on premises, hybrid or multicloud — across platforms or regions is a challenge that IBM® Big Replicate LiveData Migrator was built to handle. Powered by a high-performance coordination engine, it uses consensus to keep unstructured data accessible, accurate and consistent regardless of the environment.

Big Replicate LiveData Migrator enables enterprises to create an environment where data is always available, accurate, and protected, creating a strong backbone for their IT infrastructure and a foundation for running consistent, accurate machine learning applications. With zero downtime and data loss, LiveData Migrator handles everything in the background without requiring involvement from the customer.

With the integration of LiveData Migrator, Big Replicate users can now start migrating petabyte-scale data within minutes without needing help from engineers or other consultants, even while the source data sets are under active change, ensuring that any ongoing data changes are replicated to the target environment. Data migration risks are mitigated with immediate, live and scalable migration delivered with LiveData Migrator. Also minimized is the involvement of IT resources through automated migration capabilities.  No changes are required to applications, cluster or node configuration, or operation while data changes are migrated continuously and completely.

Learn more about IBM Big Replicate today and prepare to take on your own data growth in hybrid architectures. You can also schedule a free one-on-one consultation with one of our experts to ask any questions you might have.

  1. 6 Predictions About Data In 2020 And The Coming Decade,” January 6, 2020. Gil Press, Forbes.
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