According to Gartner, over 90% of deployed data lakes will become useless as they are overwhelmed with information assets captured for uncertain use cases. This is indeed, an alarming situation. While companies are spending time and money to unify their data, how does one find value in that unified data? How can companies garner valuable insights from their data?
The idea of the data lake has been gaining traction since 2011-2012. At the time, organizations were enticed with the prospect of analyzing big data on the fly and boosting business agility. This was further cemented by the cheap availability of cloud storage.
But the promises were not as rosy and simple to fulfil. Data scientists were faced with the challenge of replicating results of testbeds to real-world scenarios. Soon, they were faced with a handful of other challenges including and not limited to data ingestion, preparation for quick querying and formation of data swamps.
Big data, without a shadow of a doubt, is an invaluable resource, but it is extremely complex at the same time–a fact that platform and service providers often do not admit. The inherent complexities in the architecture for consolidating big data from disparate sources (both batch and streaming) into a data lake and using it to achieve desired results, has given rise to data lake automation. It is a practice that aims to eliminate most of the challenges by equipping data lake platforms with the power to self-heal and self-tune, create partitions for quick querying, and much more.
Data Lake Trends
A data lake allows organizations to store huge amounts of diverse datasets without having to build a model first.
Data lakes are especially useful when data managers are looking for ways to capture and store data from a variety of sources in various formats. In a lot of instances, data lakes are considered cost effective, and are used with the intent to store data for exploratory analysis, maintain the data quality and quantity simultaneously, and help eliminate disparities or incompatibilities when it comes to legacy data storage systems.
Having said that, while many enterprises might be thinking about incorporating data lakes, they struggle with identifying what to consider before taking the journey. Managing data lakes can be an extremely complex process; and it is important to streamline data discovery, provide insights delivery and administration of data lake platforms such as Microsoft Azure, Snowflake, Hadoop, Google Cloud, and AWS, while at the same time help maintain security and privacy measures to safeguard your data.
Since the pandemic started, we’re seeing companies accelerate their digital transformation journey. They are looking at a faster transition toward the cloud and becoming digital. However, in order to do that they need the right data strategy and roadmap, capability to drive analytical insights, seamless transition to cloud platforms, and drive automation to instrument these insights.
Impediments to Realizing Data Lake Potential
While most enterprises understand that concepts such as data lakes are important, let’s face it–there are many problems companies encounter when it comes to data lake deployment.
For one, many data scientists underestimate the complexities of developing a data lake and the expertise required to overcome some of those complexities. Companies also face the challenge of overcoming the ongoing costs attached. Difficulty in analyzing diverse data sets, data security, data silos are all real-challenges that companies today face.
Data lake ingestion is a critical component – Consolidating high volumes of data from various sources into a data lake is a difficult task leading to inconsistencies with formats of data. To add to it, rapidly changing data makes the process even more difficult. Many face lags with data updating, and new insights being produced regularly.
Agility gets compromised – Today, the problem with big data is the size of data sets and streamlining databases which makes it difficult for the data lakes to process data efficiently.
Preparation of data and accessibility – The problem arises when data is lumped together and there is not enough idea as to what needs to be linked where, and the types of information that needs to be available to everyone in an organization.
Making Data Lakes More Performant and Useful
Nowadays, a more modern approach is required to make data lakes performant. This can be done by enterprises by automating their data pipeline–right from data ingestion, to continuous updates, providing faster time-to-value to creating analytics-ready data sets.
With automation, the challenges that emerge with simply dumping data into lakes turning them into data swamps are done away with. Data swamps are undocumented and difficult to leverage and navigate. Automation provides an agile approach to data lake development helping companies get rid of data swamps, implement ready-reference architectures, onboard use cases quicker, automate legwork, ensure better governance and establish a data-friendly bottom-line as well as culture for an enterprise.
For example, in the telecommunications industry, automation of data lakes can help handle repetitive tasks with respect to customer relationship building and management, automate error remediation, create a single source of truth, and improve service quality.
Now with automation, it is becoming easier to leverage the compute power of data lakes helping enterprises simplify much of the data lake development and management process. By automating data lakes wherever possible – either when it comes to data ingestion or data preparation, as well as making data query-ready, enterprises can look at reviving their data lakes and bring back some of its lost appeal.
Automation Through Modern and Scalable Data Architectures
When it comes to considering deployment environments and data formats, many enterprises look at running hybrid data environments–which are becoming increasingly popular today–while others consider service providers and incorporating data lakes as-a-service.
An emerging category, data lakes as-a-service, are useful for enterprises who want to consider pre-built cloud services and have a service provider install and manage the technology for them. What this means for enterprises is that they are provided with:
- automated provisioning
- advanced analytics on data of any size and ingest real-time data at scale
- data lake strategy development and roadmap
- scalable data storage
- data integration, access and services
- data lake implementation and go-live enablement
- metadata management and governance
- end-to-end data security with encryption
The end goal for enterprises is generating value from lakes, and while some have succeeded, many have struggled with identifying the right recipe for success due to software processes issues or becoming overwhelmed with the size of their lakes. However, a lot is changing now due to automation, modern BI tools, and effective data management architectures. And with enterprises having specific goals in mind, there is now a better understanding of what they, too, are looking for at the bottom of the lake. It is clear that architectures will continue to evolve based on customer and enterprise data landscape demands.
In years to come, data lakes will continue to evolve and play an increasingly important role in enterprise data strategies. Organizations need reliable next-gen technology solutions in-line with their business vision and now with innovative approaches to data management, enterprises can realize their true potential by taking into account effective data architecture, digital maturity and hosting environments.
About the author: Chetan Alsisaria is the CEO & co-founder of Polestar Solutions & Services Pvt Ltd. Over the past 17 years, Alsisaria has led many technology-driven business transformation engagements for clients across the globe for Fortune 500 companies, large/mid-size organizations, new-age companies as well as in the government sector. Chetan’s area of expertise lies in identifying strategic growth areas, forming alliances, building high potential motivated teams, and delivering excellence in the areas of data analytics and enterprise performance management.