Hadoop brought Big Data to businesses, enabling cost-effective, high-performance analytics on massive datasets for the first time.
At one point last decade, every Fortune 50 company had either deployed a petabyte-scale Hadoop cluster running the open-source technology, or was making plans to do so.
Hadoop’s heyday was brief. Managing Hadoop data lakes proved laborious and expensive. And cloud-native alternatives arose that were cheaper, faster, and easier to manage and scale.
Many open-source technologies originally spawned or popularized by Hadoop continue to thrive: Spark, HBase, Hive, Impala, and more.
But the heart of classic Hadoop — the MapReduce analytics framework, along with the Hadoop Distributed File System (HDFS), YARN task scheduler and Hadoop Common Java libraries — is considered legacy today.
Google, which helped create Hadoop back in 2004, got rid of MapReduce more than eight years ago. Twitter followed suit in 2018. Even Cloudera, the leading champion of Hadoop to businesses, had by 2015 switched its default analytics engine from MapReduce to Spark.
What’s a Hadoop User to do?
Naturally, migration has been on the minds of the many companies still using Hadoop. Perhaps yours is one of them. If so, there are several paths forward for you:
- Rebuild your on-premises Hadoop clusters in the public cloud. The three largest public cloud providers all provide managed hosted clusters of Hadoop: Amazon EMR, Azure HDInsight, and Google DataProc which they say run big data applications faster, less expensively, and with lower ops than on-premises Hadoop.
- Migrate to a new on-premises or hybrid cloud alternative. These generally claim better performance, lower cost and reduced management than on-premises Hadoop, though with more transformation than simply uploading Hadoop into the cloud. Examples include Singlestore (formerly MemSQL) and the Cloudera Data Platform (CDP).
- Migrate to a modern cloud-native data warehouse. Upgrading to a serverless platform such as Databricks, Snowflake, Google BigQuery, Amazon RedShift promises to deliver real-time performance, automatic scalability, and low ops.
Beyond the hype, there are potential downsides to consider with each approach. Migrating to a public cloud is the easiest migration path. Easy is relative, however.
Any migration involving many TB or even PB of data and mission-critical analytics should be carefully planned and tested to avoid lost or erroneous data, malfunctioning data pipelines, ballooning costs, and other catastrophes.
And simply rehosting your on-premises Hadoop infrastructure in the cloud means that you will miss out on the cost and performance benefits of refactoring your data infrastructure for the latest microservices-based, serverless data stack.
Migrating off Hadoop to a modern alternative will require even more planning and work than moving Hadoop into the cloud. The benefits are much higher, but so are the risks to your data and analytical workloads.
And in all three scenarios, rushing your migration and doing it all at once rather than in well-planned and tested phases increases the chances of disaster, as well as being locked into an infrastructure that doesn’t best serve your business’s needs.
A Better Way
Hadoop users do not have to make hurried, badly-planned migration. Deploying a multi-dimensional data observability platform such as Acceldata provides powerful performance management features for Hadoop and other Big Data environments.
Acceldata empowers your data engineers with visibility, control, and ML-driven automation that prevents Hadoop data outages, ensures reliable data, helps you manage your HDFS clusters and monitor your MapReduce queries, as well as cut costs.
For Hadoop administrators, our platform provides in-flight, correlated alerts over 2,000+ metrics that gives them time to react and respond with confidence. When immediate, automated action is required, Acceldata can also support several actions out of the box to help you enforce and meet SLAs. Some examples include:
- Killing an application when it exceeds a duration, memory bound or other metric
- Reducing the priority of an application to maintain the performance of mission-critical ones
- Resuming or resubmitting the same job
- Custom workflow integration to prevent outages in case the number of concurrent users grows
- Interception of poorly-written SQLs
Your data engineers can take over management of their Hadoop environments with confidence and without onerous effort. Not only will they be able to extend your Hadoop environment, they may very well optimize it!
And because Acceldata manages on-premises and cloud environments, and integrates with a wide variety of environments including S3, Kafka, Spark, Pulsar, Google Cloud, Druid, Databricks, Snowflake and more, you’ll have a technical co-pilot for your Hadoop migration, whichever platform you choose.
Acceldata helps you validate and reconcile data before and after it is migrated. These data profiles ensure your data remains high quality. Acceldata also helps you rebuild your data pipelines in your new environment by making it easy to find trusted data. Acceldata can also help you move your Spark cluster from Hadoop YARN to the more scalable, flexible Kubernetes.
Acceldata can also help you stress test newly-built data pipelines and predict if bottlenecks will occur.
These features all ease the planning, testing, and enablement of a successful Hadoop migration whenever you want to make that happen. With Acceldata, you’ll have the ability to choose the right migration scenario that meets your business’s needs, budget, and timeline.