Hadoop is a widely used open-source computing framework that was created by the Apache Foundation in 2005 to process and analyze large datasets. It has gained popularity for its ability to handle large amounts of data and scale horizontally, making it a valuable tool for organizations seeking to extract insights from their data.
However, despite its advantages, Hadoop also poses its own set of challenges that can confuse data teams and create risks in the environment. To ensure the security and integrity of data, it is crucial to address these risks.
In this blog post, we will explore some best practices that can help minimize the risks associated with using Hadoop. Additionally, we will discuss steps that data engineers can take to optimize their data environments, which may involve transferring some data away from Hadoop.
Why Data Teams Rely on Hadoop
To provide some context, let’s briefly review the main components and use cases of Hadoop. Hadoop is built on two key components: the Hadoop Distributed File System (HDFS) and the MapReduce programming model. HDFS is a fault-tolerant distributed file system that enables high-throughput access to data. It follows a primary/secondary architecture where the NameNode acts as the master to manage the file system namespace, while DataNodes act as the slaves and store the actual data.
The MapReduce programming model is a parallel processing framework used for distributed data processing. It works in two phases: the map phase, where data is read from the input source and processed in parallel across multiple nodes, and the reduce phase, where the output of the map phase is combined and processed to produce the final result.
In addition to HDFS and MapReduce, Hadoop includes various other components and tools that together create a complete data platform. YARN (Yet Another Resource Negotiator) is a resource management layer that manages cluster resources and allows for the execution of other distributed applications on top of Hadoop. Other tools such as Hive provide a SQL-like interface for querying Hadoop data, Pig is a high-level programming language used for processing large datasets, and HBase is a NoSQL database that runs on top of Hadoop and provides real-time access to data.
Risks of Using Hadoop
Hadoop poses several risks to data teams, with one of the main challenges being its complexity as a distributed system, which can make management and maintenance difficult. This can result in operational issues such as system downtime, data loss, and other problems that can negatively affect business operations.
Moreover, the setup and maintenance of Hadoop require a significant investment in hardware, software, and technical expertise, making it a costly solution for smaller organizations with limited resources.
Hadoop’s steep learning curve is also a risk, as it can necessitate extensive training for data teams to become proficient in its use, resulting in delays and additional costs for organizations.
Furthermore, there are potential security risks associated with Hadoop. Due to its capability of handling large volumes of data from multiple sources, it can be more vulnerable to cyberattacks and data breaches. To minimize such risks, data teams must implement robust security measures and ensure that their Hadoop clusters are appropriately configured and maintained.
The Hadoop ecosystem is continuously evolving, with new tools and technologies being introduced regularly. Although this provides advantages, it can be a challenge for data teams 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 Reduce Hadoop Risk
Below are crucial steps that data engineering teams should take to ensure optimal operation of their Hadoop environments. These steps are not merely recommendations; rather, they are essential practices that we have observed in every well-functioning Hadoop environment. It would be wise to bake these steps into the foundation of your data infrastructure.
Keep Your Hadoop Cluster Updated: To reduce the risk of vulnerabilities, it is critical to maintain your Hadoop cluster by applying the latest security patches and bug fixes. Leading Hadoop distributions such as Cloudera and Hortonworks frequently release updates that not only address security issues but also introduce new features. By regularly applying these updates, you can enhance the security of your Hadoop cluster.
Secure the Access to Your Hadoop Cluster: To prevent unauthorized access to your data, it is imperative to secure access to your Hadoop cluster. Strong passwords and multi-factor authentication (MFA) should be utilized to mitigate unauthorized access. Furthermore, network segmentation and firewall rules can be implemented to limit access to your cluster to authorized users and applications.
Encrypt Sensitive Data: Encrypting sensitive data in your Hadoop cluster is a critical measure to safeguard it from unauthorized access. It is recommended to use encryption at rest to protect data stored on disk and encryption in transit to protect data transmitted between nodes in the cluster. Hadoop comes with built-in encryption support, and third-party tools such as Apache Ranger can be used to manage access control and encryption policies.
Implement Role-based Access Control: Implementing role-based access control (RBAC) in a Hadoop cluster is an effective approach for regulating access to resources based on assigned roles of users or groups. This not only enhances the efficient management of data access but also minimizes the possibility of unauthorized access. Apache Ranger or Apache Sentry are two tools that can be utilized to implement RBAC in your Hadoop cluster.
Monitor Hadoop Clusters: Regular monitoring of your Hadoop cluster is critical to promptly detect and respond to any security incidents. Tools such as Apache Ambari or Cloudera Manager can help you monitor the cluster’s health and performance. Additionally, security-focused monitoring tools like Apache Knox or Apache NiFi can be utilized to monitor access to the cluster and promptly identify any suspicious activity.
Another Option — Migrate Away From Hadoop
If your company is still using Hadoop, you may be considering migration as the next step. There are several options available to you:
One option is to rebuild your on-premises Hadoop clusters in the public cloud. Amazon EMR, Azure HDInsight, and Google DataProc, the three major public cloud providers, offer managed hosted Hadoop clusters that offer faster performance, lower costs, and reduced operations compared to on-premises Hadoop.
Another option is to 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. For example, Singlestore (formerly MemSQL) and the Cloudera Data Platform (CDP) are both options. You may also want to consider the tools and repositories available in Acceldata’s Open Source Data Platform Project on Github, which offers various repositories for Apache and other projects.
A third option is to migrate to a modern, cloud-native data warehouse. Upgrading to a serverless platform like Databricks, Snowflake, Google BigQuery, or Amazon RedShift can offer real-time performance, automatic scalability, and low operations.
However, it’s important to consider the potential downsides of each approach. Migrating to the public cloud may seem like the easiest option, but careful planning and testing are still necessary 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, the risks to your data and analytical workloads are also significant.
Regardless of the 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 best serve your business needs. Therefore, it’s crucial to plan and test your migration in well-defined phases to ensure a smooth transition.
A Path to Minimizing Hadoop Risk
If you are considering migrating away from Hadoop, it’s important not to rush the process. Instead, consider using the Acceldata Data Observability platform to help manage your Hadoop environment and ensure a successful migration. Data observability can be the most important tool to support your efforts.
With Acceldata, you can empower your data engineers with powerful performance management features for Hadoop and other Big Data environments. This includes ML-driven automation, visibility, and control that prevent data outages, ensure reliable data, and help manage your HDFS clusters while cutting costs.
The platform provides in-flight, correlated alerts over 2,000+ metrics, giving Hadoop administrators the time to react and respond with confidence. Additionally, Acceldata supports several out-of-the-box actions to help enforce and meet SLAs, such as killing an application when it exceeds a duration or memory bound, reducing the priority of an application to maintain the performance of mission-critical ones, resuming or resubmitting the same job, and intercepting 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.
The platform also helps you validate and reconcile data before and after migration, ensuring high data quality. Acceldata even makes it easy to rebuild your data pipelines in your new environment by helping you find trusted data and 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, easing the planning, testing, and enablement of a successful Hadoop migration whenever you are ready to make it happen. Choose the 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 to manage your Hadoop environment with visibility, control, and ML-driven automation that prevent data outages, ensure reliable data, and help you migrate 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.