Growing relevance of data lakes
In industry parlance, data lake refers to a repository of data that brings together variety of data available, such as structured (relational), semi-structured (XML, JSON, etc.) and unstructured (Images, audio and video). It provides a way to store all the raw data together in a single place at a low cost. Distributed file systems such as Hadoop file system (HDFS) often serves this purpose. The adoption of Hadoop as the platform created the first version of data lake. The use of cloud based data lake i.e. the latest version of data lake, gained momentum in the market because of the challenges in storage flexibility, resource management, data protection deep-rooted in Hadoop-based data lakes.
Industry experts anticipate that in the coming years, majority of large organizations will experience more data flowing from data lakes to data warehouses. This prediction shows the change in the mindset of organizations which were typically driven by enterprise data warehouse as the de facto standard for their data need. As the popularity of data lakes grows, organizations face a bigger challenge of maintaining an infinite data lake. If the data in a lake is not well curated, then this data lake may turn into a data swamp, flooding an organization with information that is difficult to locate and consume.
How to keep data lakes relevant
Digital transformation requires identifying authentic and accurate data sources in an organization to truly capitalize on increasing volumes of data and generate new insights that propel growth while maintaining a single version of truth.
Following are some of the ways to keep data lakes dynamic, immaculate and viable:
1. Identify and define organization’s data goal: One of the most important preemptive steps in avoiding an organizational data swamp is to set clear boundaries for the type of information organizations are trying to collect, and their intent of what they want to do with it. Amassing a lot of data should not be the sole aim of organizations. They need to have a clarity on what they want to attain from the data they are collecting. An enterprise with a clear data strategy shall reap benefits in terms of avoiding data silos, incorporating a data driven culture to maintain customer centricity, scale up and meet demands of the modern day data environment.
2. Incorporate modern data architecture: The old data architecture models are not sufficient and may not satisfy the needs of today’s data - driven businesses in a cost effective manner. These workflows give a primer to ensure modern data architecture:
- Data profiling: Today, organizations are facing an immense challenge in retaining and re-using the huge amount of unstructured data stored by them. Data profiling allows building understanding of current data assets and their condition (data quality and lineage). It helps in scanning, classifying, indexing structured and unstructured data residing in different sources. This provides the organization an information database that can be better managed and yields maximum value in future. It is recommended that profiled data should be loaded into different curated zones (loading, staging, certified and consumption) for ease of understanding and to avoid creating a data swamp.
- Data cataloguing: There is a huge possibility that data collected in one business section may provide value to other business sections in other scenarios. However, business users may not always have the requisite visibility or awareness about which data exists and its corresponding ownership. As a result, data is seldom used beyond its context, and plenty of opportunities to extract maximum value from data are lost. One approach to resolve this drawback is to make a data catalog for the organization. A data catalog will help in managing and maintaining the existing datasets and KPI glossary to foster easy search of all the available data within the organization for users to seek maximum benefit out of it.
- Data backup and data archival: Data backup protects organization’s active and inactive data on the cloud whereas, data archive solutions are aimed for continuous data possession at minimal cost for longer periods. All cloud vendors have tools and technologies to achieve a robust archival process for the organization.
3. Build sound data governance, privacy and security: Data governance and metadata management is a critical step to keep a healthy and effective data lake strategy. A well-curated data lake contains data that’s clean, easily accessible, trusted and secure. As a result, this high-quality data can be easily consumed with confidence by the business users. It is of utmost importance to establish responsibility for data.
4. Leverage automation and AI: Due to the variety and velocity of data coming into data lake, it is important to automate the data acquisition and transformation processes. Organizations can leverage next generation data integration and enterprise data warehousing (EDW) tools along with artificial intelligence (AI) and machine learning that can help them classify, analyze and learn from the data at a high-speed with better accuracy.
5. Integrate dev/ops: DevOps processes will go hand on hand with building and maintaining a healthy data lake. Ensure clear guidelines are established on where and how data is collected to prevent “data wildness,” and make sure those standards are always followed. Take time to evaluate sources as “trustworthy,” and take preventive steps to ensure it stays that way. A little work on the front end will prove highly valuable when it comes to putting data to use.