Building a future proof data lake tech stack is often considered a complex task, and it requires the integration of several different technologies for the purposes of data storage, ingestion, processing, operations, governance, security, and data analytics.
Choosing a Data Lake Technology Stack
When choosing the right data lake technology stack, it is imperative you choose technologies with an eye on the future. The stack should be scalable and modular, so you can incorporate different upgrades or functions to satisfy your future needs. Due to the ever-changing data landscape, it is important to safeguard for your future data analytics.
On-Premise Versus Cloud- The Next Generation
When looking at a modern data infrastructure, you want to consider options to integrate both on-premise and cloud storage. These are both important to enterprise-wide data lakes because they add a better level of data security and privacy.
However, it is important to note the cloud is just as important to the data lake because it is scalable with elastic storage and allows for the computing resources an enterprise needs for large-scale processing and data storage. It does this without the need for any maintenance on expensive infrastructure.
Additionally, as big data tools continuously evolve, cloud-based data lakes can then be used by the company to evaluate new tools either in the cloud or an on-prem service in the future.
Options Available for Data Lake Storage
HDFS (Hadoop Distributed File System)
This option is for on-prem data lakes and is the storage option of choice due to its distributed data with replication. This feature allows for quick processing of big data, and it also allows the enterprise to create storage tiers for data lifecycle management.
Using these tiers will help save on costs and will also effectively maintain retention policies and other regulatory requirements. It has a NameNode and DataNode architecture that is highly scalable with high-performance access to data across the Hadoop clusters.
Cloud-based storage allows for the decoupling of storage while enabling enterprises to cut their storage costs and leverage different computing powers to meet their own demand. As previously mentioned, cloud-based storage is also advantageous because it allows an enterprise to create tiered storage for data retention.
Processing Technology for Data Lakes
These are a special type of computational cluster specifically designed to store and analyze significant amounts of unstructured data in a distributed computing environment. When referring to a cluster, we are talking about a collection of nodes. A node is a process running on a virtual or physical machine.
Hadoop is only linearly scalable and allows for a suitable platform for big data management and analytics applications. A Hadoop enterprise data lake can be used to complement a data warehouse and offload some data.
It is commonly used in an on-prem data lake and can also be developed in the cloud to create a hybrid data lake, enterprise-wide, using single distribution. For example, Hortonworks, Cloudera, and MapR.
Apache Spark is a faster engine which can be used for more large-scale data processing with additional option of using in-memory computing. Spark clusters can run on the Hadoop Platform, Mesos, or in-cloud. Additionally, they can run on their own in a standalone environment to help create a more unified compute layer across the entire enterprise.
For easier implementation of batch and streaming data processing jobs on top of the processing cluster, you can use Apache Beam. With Apache Beam, enterprises can develop their very own data processing pipelines, or use automated tools such as Cornerstone, and the runner (processing engine) can be virtually anything including Direct Runner, Apache Apex, Apache Flink, Apache Gearpump, Spark, and Google Cloud Dataflow.
The design also allows the pipeline to be more portable across all the different runners while providing the enterprise with the flexibility and leverage it needs to better future-proof its data processing requirements.
The Data Management Platform
The technology stack vital to a successful data lake may be a bit complex and extensive, but you must consider how enterprises are able to manage data across such a complex and complicated technology stack and this is where the data management platform comes into play.
Having the right data management platform and applications are essential to allowing the enterprises to manage and track the data across all the fields of storage. It also will enable it to compute and process layers throughout the entire lifecycle of the data platform.
The transparency of the data platform leads to reduced data prep times, far easier data discovery, and access to much faster business insights. Finally, it ensures the enterprises are able to meet all regulatory requirements regarding data privacy, security, and data governance.