Hadoop is a popular open source computing framework for processing and analyzing large datasets. Originally created in 2005 by the Apache Foundation, it has been adopted for distributed computing systems. Hadoop is widely used in industry for big data analytics and other data-intensive applications. Its ability to scale horizontally and handle large amounts of data makes it a valuable tool for organizations looking to extract insights from their data.
While Hadoop is helping organizations manage and scale their massive data environments, it also has its own set of challenges that at best, befuddle data teams. At worst, it can present risk into an environment.
These risks need to be addressed to ensure the security and integrity of your data. In this blog, we’ll look at some best practices to minimize the risk of using Hadoop, as well as some steps that data engineers can take to optimize their data environments, which may or may not include moving some data away from Hadoop.
Why do data teams use Hadoop?
First, here’s some context - you likely know most of this, but as with any complex system, it’s good to set the table so we all know the terms and use cases being discussed.
Hadoop is based on two main components: the Hadoop Distributed File System (HDFS) and the MapReduce programming model. HDFS is a distributed file system that provides high-throughput access to data and is designed to be fault-tolerant. It is based on the Google File System (GFS) and uses a primary/secondary architecture, where the NameNode acts as the master and manages the file system namespace and the DataNodes act as the slaves and store the actual data.
The MapReduce programming model is a parallel processing framework for distributed data processing. It consists of two phases: the map phase and the reduce phase. In the map phase, data is read from the input data source and processed in parallel across multiple nodes in the cluster. In the reduce phase, the output of the map phase is combined and processed to produce the final result.
Hadoop also includes several other components and tools that make it a complete data platform, including YARN (Yet Another Resource Negotiator), which is a resource management layer that manages cluster resources and enables the running of other distributed applications on top of Hadoop. Other tools include Hive, which provides a SQL-like interface for querying Hadoop data, Pig, which is a high-level programming language for processing large datasets, and HBase, which is a NoSQL database that runs on top of Hadoop and provides real-time access to data.
What are the risks of using Hadoop?
Hadoop can be risky for data teams for several reasons. One of the main challenges is that Hadoop is a complex and distributed system, which can make it difficult to manage and maintain. This can lead to issues such as data loss, system downtime, and other operational problems that can impact business operations.
Another Hadoop risk is that it requires a significant investment in hardware, software, and technical expertise to set up and maintain. This can make it prohibitively expensive for smaller organizations with limited resources.
As every Hadoop user will tell you, it has a steep learning curve, and data teams may require extensive training to become proficient in its use. This can result in delays and additional costs as organizations work to get their data teams up to speed on Hadoop.
Additionally, there are potential security risks associated with Hadoop. The system is designed to handle large volumes of data from multiple sources, which can make it more vulnerable to cyberattacks and data breaches. To minimize these risks, data teams need to implement robust security measures and ensure that their Hadoop clusters are properly configured and maintained.
The Hadoop ecosystem is rapidly evolving, with new tools and technologies being developed and released on a regular basis. While this brings advantages, it can also stress data teams, as they have to keep up with the latest developments and ensure that they are using the most efficient and effective tools for their needs.
5 ways to minimize Hadoop risk
Here are the key steps that data engineering teams should take. While these are recommendations, we’ve seen that every Hadoop environment that operates well is adhering to these, so beyond just recommendation, you may want to consider carving these into the edifice of your building!
Keep Your Hadoop Cluster Up-to-Date
Keeping your Hadoop cluster up-to-date with the latest security patches and bug fixes is crucial to minimize the risk of vulnerabilities. Most Hadoop distributions, including Cloudera and Hortonworks, release regular updates that address security issues and add new features. Regularly applying these updates can help keep your cluster secure.
Secure Access to Your Hadoop Cluster
Securing access to your Hadoop cluster is essential to prevent unauthorized access to your data. Ensure that you use strong passwords and enable multi-factor authentication (MFA) to prevent unauthorized access. You can also use network segmentation and firewall rules to restrict access to your cluster to authorized users and applications.
Use Encryption to Protect Sensitive Data
Encrypting sensitive data in your Hadoop cluster is an essential practice to protect it from unauthorized access. Use encryption at rest to protect data stored on disk and encryption in transit to protect data in transit between nodes in the cluster. Hadoop provides native support for encryption, and you can also use third-party tools like Apache Ranger to manage access control and encryption policies.
Implement Role-Based Access Control
Role-based access control (RBAC) is a method of restricting access to resources based on the roles assigned to users or groups. Implementing RBAC in your Hadoop cluster can help you manage access to your data more efficiently and reduce the risk of unauthorized access. You can use tools like Apache Ranger or Apache Sentry to implement RBAC in your Hadoop cluster.
Monitor Your Hadoop Cluster
Monitoring your Hadoop cluster regularly is essential to detect and respond to security incidents. Use tools like Apache Ambari or Cloudera Manager to monitor the health and performance of your cluster. You can also use security-focused monitoring tools like Apache Knox or Apache NiFi to monitor access to your cluster and detect suspicious activity.
There is another way…migrating away from Hadoop
Many companies still using Hadoop are considering migration as a natural next step. If you are one of them, there are several options available:
One approach is to rebuild your on-premises Hadoop clusters in the public cloud. The three major public cloud providers - Amazon EMR, Azure HDInsight, and Google DataProc - offer managed hosted clusters of Hadoop, which promise faster performance, lower costs, and reduced operations compared to on-premises Hadoop.
Alternatively, you could migrate to a new on-premises or hybrid cloud solution. These alternatives generally claim better performance, lower costs, and reduced management compared to on-premises Hadoop. Examples include Singlestore (formerly MemSQL) and the Cloudera Data Platform (CDP). You may want to also consider tools and repositories available in Acceldata’s Open Source Data Platform Project on Github, which offers a variety of repositories for Apache and other types of projects.
A third option is to migrate to a modern, cloud-native data warehouse. Upgrading to a serverless platform such as Databricks, Snowflake, Google BigQuery, or Amazon RedShift can deliver real-time performance, automatic scalability, and low operations.
However, it is important to consider the potential downsides of each approach. Migrating to the public cloud may seem like the easiest option, but it still requires careful planning and testing to avoid potential data loss, malfunctioning data pipelines, and ballooning costs.
Simply rehosting your on-premises Hadoop infrastructure in the cloud means missing 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. While the benefits are significant, so are the risks to your data and analytical workloads.
No matter which migration path you choose, rushing the process and doing it all at once increases the chances of disaster, as well as being locked into an infrastructure that may not serve your business needs best. Therefore, it is crucial to plan and test your migration in well-defined phases to ensure a smooth transition.
How to minimize Hadoop migration risk
If you are still using Hadoop and want to migrate to a better solution, don't rush it! The Acceldata Data Observability platform can help you manage your Hadoop environment and ensure a successful migration.
Acceldata provides your data engineers with powerful performance management features for Hadoop and other Big Data environments. With Acceldata, you will have visibility, control, and ML-driven automation that prevent Hadoop data outages, ensure reliable data, help you manage your HDFS clusters, and monitor your MapReduce queries, all while cutting costs.
The platform offers in-flight, correlated alerts over 2,000+ metrics that give your Hadoop administrators time to react and respond with confidence. If immediate, automated action is required, Acceldata can support several out-of-the-box actions to help enforce and meet SLAs. For example, it can kill an application when it exceeds a duration or memory bound, reduce the priority of an application to maintain the performance of mission-critical ones, resume or resubmit the same job, and intercept poorly-written SQLs.
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. This means that you will 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 that your data remains of high quality. The platform also helps you rebuild your data pipelines in your new environment by making it easy to find trusted data. It can even help you move your Spark cluster from Hadoop YARN to the more scalable, flexible Kubernetes.
With Acceldata, you can stress test newly-built data pipelines and predict if bottlenecks will occur. These features ease the planning, testing, and enablement of a successful Hadoop migration whenever you are ready to make it happen. You can choose the right migration scenario that meets your business's needs, budget, and timeline with confidence.
Don’t rush your Hadoop migration. Deploy Acceldata and empower your data engineers with visibility, control, and ML-driven automation that prevent Hadoop data outages, ensure reliable data, and help you manage your Hadoop environment with confidence.
Get data observability for HDP and learn how to improve performance, scale, reliability, efficiency, and overall TCO of their Hadoop environments with the Acceldata Data Observability platform.