Sponsor: WANdisco

The cloud is a natural home for big data. In the cloud, companies can take advantage of cheap, scalable storage and compute platforms leveraging an OPEX model. Cloud Service Providers (CSPs) are able to spin massive compute clusters up or down automatically, helping organizations optimize costs and reduce the need for people with deep expertise in managing such deployments. CSPs have also enhanced their big data solutions providing greater functionality over native Hadoop offerings, and greatly simplifying the complexities for organizations having to manage their own Hadoop implementations.

This eBook explores some of the business risks associated with big data to cloud migrations. It highlights third-party research as well as results of a survey conducted with 80 cloud and data architects, and other technical specialists to provide insights on their data migration plans and strategies, and compares the costs and risks associated with manual vs automated big data migrations.

Over the past few years, distributed data caches and cluster-based distributed data environments became popular with large scale IT systems. Hadoop now provides the data scalability solution to Facebook and many other large systems. Apache designed Hadoop to scale up from single servers to thousands of machines, each offering local computation and storage. Now, customers are looking to migrate their on-premises Hadoop systems to public cloud environments, but need to minimize risk, business disruption, and cost.

This paper examines the functional and performance differences between two Hadoop migration tools: WANdisco LiveMigrator and Cloudera BDR.

With WANdisco's LiveData approach to big data migration to the cloud, there is no application downtime during migration and no data inconsistencies, even when data sets are under active change.

This whitepaper will explore an effective, automated approach to cloud, on-premises and multi-vendor Hadoop migrations.

Data lakes are becoming ever more tightly integrated into essential operational services. For born-in-the cloud enterprises, dialing up capacity to expand analytics services is relatively straightforward. However, for businesses operating on-premises data lakes, expansion can be costly and slow—which puts them at a significant competitive disadvantage.

For these enterprises, migrating on-premises data lakes to the public cloud promises a cost-effective way to gain leading-edge analytics capabilities and practically unlimited scalability. However, the perceived need for an extended period of downtime during the cloud migration can make these projects seem too costly, painful and risky.

In this eBook, WANdisco explains why continuous data replication is the key to achieving a low-risk, seamless data lake migration. With the WANdisco Fusion platform enforcing strong consistency between data on premises and in the cloud, businesses can run both environments in parallel for as long as they need to—and switch seamlessly to the cloud as soon as they are ready. The result is a simple, automated Hadoop migration with no disruption to the business.

Data lakes are becoming ever more tightly integrated into essential operational services. For born-in-the cloud enterprises, dialing up capacity to expand analytics services is relatively straightforward. However, for businesses operating on-premises data lakes, expansion can be costly and slow—which puts them at a significant competitive disadvantage.

For these enterprises, migrating on-premises data lakes to the public cloud promises a cost-effective way to gain leading-edge analytics capabilities and practically unlimited scalability. However, the perceived need for an extended period of downtime during the cloud migration can make these projects seem too costly, painful and risky.

In this eBook, WANdisco explains why continuous data replication is the key to achieving a low-risk, seamless data lake migration. With the WANdisco Fusion platform enforcing strong consistency between data on premises and in the cloud, businesses can run both environments in parallel for as long as they need to—and switch seamlessly to the cloud as soon as they are ready. The result is a simple, automated Hadoop migration with no disruption to the business.

Over the past few years, distributed data caches and cluster-based distributed data environments became popular with large scale IT systems. Hadoop now provides the data scalability solution to Facebook and many other large systems. Apache designed Hadoop to scale up from single servers to thousands of machines, each offering local computation and storage. Now, customers are looking to migrate their on-premises Hadoop systems to public cloud environments, but need to minimize risk, business disruption, and cost.

This paper examines the functional and performance differences between two Hadoop migration tools: WANdisco LiveMigrator and Cloudera BDR.

With WANdisco's LiveData approach to big data migration to the cloud, there is no application downtime during migration and no data inconsistencies, even when data sets are under active change.

This whitepaper will explore an effective, automated approach to cloud, on-premises and multi-vendor Hadoop migrations.

The cloud is a natural home for big data. In the cloud, companies can take advantage of cheap, scalable storage and compute platforms leveraging an OPEX model. Cloud Service Providers (CSPs) are able to spin massive compute clusters up or down automatically, helping organizations optimize costs and reduce the need for people with deep expertise in managing such deployments. CSPs have also enhanced their big data solutions providing greater functionality over native Hadoop offerings, and greatly simplifying the complexities for organizations having to manage their own Hadoop implementations.

This eBook explores some of the business risks associated with big data to cloud migrations. It highlights third-party research as well as results of a survey conducted with 80 cloud and data architects, and other technical specialists to provide insights on their data migration plans and strategies, and compares the costs and risks associated with manual vs automated big data migrations.

Data lakes are becoming ever more tightly integrated into essential operational services. For born-in-the cloud enterprises, dialing up capacity to expand analytics services is relatively straightforward. However, for businesses operating on-premises data lakes, expansion can be costly and slow—which puts them at a significant competitive disadvantage.

For these enterprises, migrating on-premises data lakes to the public cloud promises a cost-effective way to gain leading-edge analytics capabilities and practically unlimited scalability. However, the perceived need for an extended period of downtime during the cloud migration can make these projects seem too costly, painful and risky.

In this eBook, WANdisco explains why continuous data replication is the key to achieving a low-risk, seamless data lake migration. With the WANdisco Fusion platform enforcing strong consistency between data on premises and in the cloud, businesses can run both environments in parallel for as long as they need to—and switch seamlessly to the cloud as soon as they are ready. The result is a simple, automated Hadoop migration with no disruption to the business.

Over the past few years, distributed data caches and cluster-based distributed data environments became popular with large scale IT systems. Hadoop now provides the data scalability solution to Facebook and many other large systems. Apache designed Hadoop to scale up from single servers to thousands of machines, each offering local computation and storage. Now, customers are looking to migrate their on-premises Hadoop systems to public cloud environments, but need to minimize risk, business disruption, and cost.

This paper examines the functional and performance differences between two Hadoop migration tools: WANdisco LiveMigrator and Cloudera BDR.

With WANdisco's LiveData approach to big data migration to the cloud, there is no application downtime during migration and no data inconsistencies, even when data sets are under active change.

This whitepaper will explore an effective, automated approach to cloud, on-premises and multi-vendor Hadoop migrations.

The cloud is a natural home for big data. In the cloud, companies can take advantage of cheap, scalable storage and compute platforms leveraging an OPEX model. Cloud Service Providers (CSPs) are able to spin massive compute clusters up or down automatically, helping organizations optimize costs and reduce the need for people with deep expertise in managing such deployments. CSPs have also enhanced their big data solutions providing greater functionality over native Hadoop offerings, and greatly simplifying the complexities for organizations having to manage their own Hadoop implementations.

This eBook explores some of the business risks associated with big data to cloud migrations. It highlights third-party research as well as results of a survey conducted with 80 cloud and data architects, and other technical specialists to provide insights on their data migration plans and strategies, and compares the costs and risks associated with manual vs automated big data migrations.