Data Lake Governance Best Practices

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December 19, 2023 by Updated April 16th, 2024

In today’s digital landscape where tons and tons of data are being generated every day, it becomes critical to manage this data flow and ensure it is secure, with the help of appropriate data management processes and procedures.

Although governments across some countries have laid out security norms such as GDPA and CCPA to preserve data, such as customers’ bank details, card details, etc, the security of data from a customer’s perspective is equally important for organizations to build customers’ trust and avoid hefty fines. This is exactly where Data Governance comes into play.

Data Governance aligns people, processes, and technology, to help them understand data to transform it into an enterprise asset. 

So, are you ready to unlock the true potential of your data? Read this blog and learn how to stay ahead of the latest data lake trends.

Learn why: Data governance and data lake security are vital for your team.

How to: Keep your data costs under control – with our Cost Explorer and Cluster Lifecycle Management.

Find out: How real-time data streaming capture can be both reliable and cost-efficient.

If you prefer to watch, rather than read: Watch the on-demand webinar here

Data Lake Governance Policy

Data Governance is all about making sure you set the right data policies so that data is accessible only to people who have the right to access it.

Data lakes can be a security threat if they are not governed properly since huge amounts of user data are added to data lakes.

Qubole offers key features to help govern their data lakes, such as:

>> Data Lake Roles: With Qubole, you can enable access controls at a minimum of three levels for effective policy management:

  1. Data ingest and data access
  2. Infrastructure and platform
  3. Data levels

>> Data Lake Encryption: Data encryption is a security method that translates data from plaintext (unencrypted) to ciphertext (encrypted). Users can access encrypted data with an encryption key and decrypted data with a decryption key.

>> Data Lake Audit Log: An audit log (also known as an audit trail) is a record of all activities and security events that occur within a computer system or network chronologically. Audit logs are typically used to track and monitor access to sensitive data, changes to system settings, and other specific events that may affect the system’s security.

Data Lake GDPR and CCPA Compliance

Delta Lake Tables and Apache Ranger address distinct requirements of granular data access control and granular delete/merge/update respectively, enabling Qubole enterprise customers to adhere to regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). These government policies ensure that user’s data is protected.

Qubole Data Services helps organizations govern data in their data lakes across multiple engines. It also makes them future-proof for newer regulations while dealing with the massive volume and velocity of data. As a built-in feature of Qubole data service, these open-source solutions are a part of everyday workflow instead of an afterthought point fix solution.

Delta Lake Tables: These are a new table type, providing immutable data lineage and ACID transactions.

  • New Table Type: Here, Mutable data is used, which can be changed. Any data changes made simply overwrite and replace the previous record. This means that previous iterations of data are lost unless there is a system of back-ups and transaction logs that track changes.
  • Immutable Data Lineage: Every change made to a Delta Lake Table is tracked and logged, making it possible to track changes to data and roll back changes.
  • ACID Transactions: ACID transactions create several locks during the course of operations. You can manage transactions and corresponding locks using a number of tools within Hive. Qubole supports Hive ACID transactions in Spark and Trinoas well. Hive ACID Transactions describes the prerequisites, supported ACID features, and Hive ACID compaction that applies to all three query engines.

Apache Ranger: Apache Ranger is a security framework that can be used to manage access to data lakes. It provides role-based access and data masking as well. It can control not only the tables but also the columns. It also helps organizations control who has access to the data.

Audit logging allows organizations to track who has accessed the data and what they have done with it. Apache Ranger stores audit logs on HDFS in JSON format uncompressed. Each line of the audit log contains a full JSON object.

Data Lake Financial Governance

Data-driven enterprises nowadays face financial governance challenges on a regular basis as the number of big data projects using the public cloud internally has risen exponentially. Data lakes can especially be expensive to maintain and operate as they are prone to cost overrun. Whilst traceability and predictability are important elements in financial governance policies, cost control, and expense reduction are usually the starting focus of any financial governance exercise.

Qubole’s data platform provides a rich set of financial governance capabilities that alleviate the pain points of implementing a big data strategy with more traditional infrastructures. Big data platforms have a bursty and unpredictable nature that tend to exacerbate the inefficiencies of an on-premises data center infrastructure. Many companies who perform a lift and shift of their infrastructure to the cloud face the same challenges in realizing their big data promise because replicating the existing setup prevents you from leveraging cloud-native functionality.

At Qubole, we provide powerful automation to control spending by optimizing resource consumption. This can be achieved with our specially designed features such as:

  • Workload-Aware Autoscaling: Workload-Aware Auto-Scaling is an alternative architectural approach to Auto-Scaling that is better suited for new classes of applications like Hadoop, Spark, and Trinothat have now become commonplace in the Cloud.
  • Intelligent Spot Management: With Qubole, you can optimize the use of Spot instances (AWS or Google), resulting in cloud computing cost savings of up to 80%. Users can use policy-based Spot instance management to balance performance, cost, and SLA requirements.
  • Heterogeneous Cluster Management: Qubole’s heterogeneous cluster configuration for on-demand and preemptive nodes allows you to pick the most cost-effective combination for your job.  Qubole enables you to configure heterogeneous clusters by mixing nodes of multiple instance types, delivering much greater data processing efficiency.
  • Cost Explorer: Qubole’s Cost Explorer is a powerful tool that helps organizations monitor, manage, and optimize big data costs by providing visibility of workloads at the user/department, job, cluster, and cluster instance levels. It also helps enterprises achieve a financial governance solution to monitor their workload expenditures using pre-built and customizable reports and visualizations. You can set the budgets, thereby providing a comprehensive view of data lake costs including areas such as:
    • Cost Per Query
    • Cost Per Cluster
    • Cost Per User
    • Cost Per Application

Cluster Lifecycle Management

Qubole helps organizations save huge amounts of money with built-in platform capabilities and sustainable economics that allow your infrastructure to scale up or down as per one’s requirement automatically. Qubole provides automated platform management for the entire cluster lifecycle: configuration, provisioning, monitoring, scaling, optimization, and recovery. The platform maintains cluster health by automatically self-adjusting based on workload size as well as proactively monitoring cluster performance.

Qubole also eliminates resource waste by automating the provisioning and de-provisioning of clusters and automatically shutting down a cluster without risk of data loss when there are no active jobs. These decisions are based on granular-level details (like task progression) and occur autonomously in real time to avoid overpaying for computing, interrupting active jobs, or missing SLAs.

Qubole’s automated platform management for cluster lifecycle management provides a range of benefits such as:

  • Cost Savings: Saves money by automatically provisioning and de-provisioning clusters
  • More Efficient: Clusters provisioned when needed and de-provisioned when not needed
  • Increased Reliability: Ensures that clusters are always available when needed
  • Improved Security: Clusters are only provisioned to authorized users. Qubole never processes the data in the Qubole environment. It is always managed at the customer’s end therefore ensuring the safety of user data.

Want to save up to 42% on your data lake costs? Learn about Qubole Cost Explorer.

Getting Started with Data Lake Governance

The growth of data usage of ad-hoc data analytics, streaming analytics, and ML may be well understood, but what remains uncertain and thus completely unpredictable is when and how often a company’s needs for data processing will spike or fall, along with the costs.

Therefore, organizations must rely on controls, automation, and intelligent policies to govern data processing and attain sustainable economics.  Through this blog, we have highlighted the various aspects of Data Governance along with the emerging Data Lake trends.

Qubole is helping organizations regain control of costs and succeed at their goals and initiatives without overpaying. The openness and data workload flexibility that Qubole offers while lowering cloud data lake costs by over 50 percent is unparalleled.  Register for our free trial to try out a new world of data governance.

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