Conventional databases are no longer the appropriate solution in a world where data volume is growing every second. Many modern businesses are adopting big data technologies like data lakes to counter data volume and velocity. Data lake infrastructures such as Apache Hadoop are designed to handle data in large capacities. These infrastructures offer benefits such as data replication for enhanced protection and multi-node computing for faster data processing. Data lakes have become a necessity since they improve decision-making abilities by providing analytics and insights from constantly updated data.

Most organizations implement data lakes as a data management solution for their growing user base, but it has its obstacles and challenges. This article will discuss why many big data initiatives fail and how aspiring firms can avoid these failures. But before diving into any of that, let’s first understand the different components that make up a data lake.

Table of Contents

Explaining Data Lake Architecture & Maturity Levels

Like data warehouses, data lakes process information in steps. Each processing layer has different responsibilities. Together these are responsible for shaping the data for use by data scientists and analysts for machine learning and dashboarding purposes. Let’s discuss each layer in detail.

Ingestion layer

This is the first step in the data lake pipeline. The ingestion layer is responsible for loading raw data from multiple data sources onto the data lake platform. The channel includes structured, unstructured, and semi-structured data and works for batch and streaming data.

The ingested data includes:

  • Tables

  • Images

  • Videos

  • Live stream data from camera feeds or IoT devices.

The purpose of the ingestion layer is to gather all of the data within the data lake infrastructure.

Distillation layer

The distillation layer provides structure to the raw data ingested in the first layer. The information has some form of processing to ensure formats and schemas across multiple tables are appropriately defined. This layer performs transformations on the metadata rather than the data itself.

Processing layer

This is where the stored data is refined. The processing layer is where user-defined queries are run on the structured data. These queries prepare data for data science and data analytics use cases such as dashboards or artificial intelligence.

The queries extract important datasets that are further utilized for specific business problems. Depending on the requirement, the processing layer can process data in real-time or in batches.

Insights layer

The insights layer is very similar to the processing layer since it acts as the output for all the processing done in the latter. This is where data is extracted and displayed on dashboards to assess business value and perform advanced analytics.

Related Reading: Data Lake vs Data Warehouse: 7 Critical Differences

Unified Operations layer

The unified operations layer is responsible for system management by ensuring all processes work smoothly. It performs auditing and proficiency monitoring to keep all systems in check.

Key Factors of Data Lake Growth

A data lake is vital to growing business for extracting valuable insights and making data-driven decisions. However, it can be a nightmare to handle and maintain if not done right. Certain vital factors govern the successful establishment and growth of a Data LakeThe essential data lake includes:

  • Robust Data Pipelines: These govern the ingestion of data into the lake. The data gets plagued with inconsistencies and inaccuracies if the ETL pipelines are not developed well.

  • Efficient And Cost-Effective Hardware: Data storage should be handled with care due to its sheer volume. The storage hardware should be powerful enough to process big data and be budget-friendly.

  • Data Security: Cyber attacks and data breaches are common on high-profile databases. A data lake contains all of an organization's data, making it vulnerable to attacks. Security is a significant concern, especially in critical domains such as healthcare.

  • Defined Business Value: Building a data lake requires resources in the form of workforce and capital. All of these efforts will be in vain if you have not planned how you wish to utilize the lake. Planning the desired outcome and then building the infrastructure accordingly is better.

Data Lake Challenges

Data lake implementations are no stranger to failures. In 2017, Gartner reported that approximately 85% of big data projects fail. This failure is often the result of a lack of research and planning. Let’s discuss some important factors that organizations overlook while building data lakes.

Lack of Quality Workforce

Data Lake infrastructures are significantly different from a data warehouse, so conventional data engineers are usually not technically competent for the task. An inexperienced workforce requires additional time and effort to grasp big data concepts. Organizations need employees with unique skills and experience, which is difficult to find.

Unstructured and Semistructured Data Problems

Unstructured and semi-structured data consist of images, videos, text, and audio files. Handling them is a big challenge because, unlike tabular data, they are difficult to interpret and store logically. Before working with unstructured and semi-structured data, it is vital to establish their business needs and intent so that the ingestion and storage pipelines can be designed accordingly.

Lack of quality/real-time Data Integrations

A data lake design aims to accommodate various types of data, which is impossible without ETL pipelines and proper data integration. Real-time data integrations are essential for making quick, informed decisions, so without them, data lakes are useless.

Lack of Governance & Security

Lack of governance leads to gaps in metadata information. Most organizations do not store metadata for tables, making it challenging to structure a data lake. Security is another vital aspect of the big data ecosystem, without which organizations can suffer huge losses.

Related Reading: What Well-Designed Data Lake Architecture Looks Like

Data Lakes Turning Into Data Swamps--Good or Bad?

Lack of governance and poor data quality turn data lakes into swamps. A data swamp consists of data with irregular structure, missing metadata information, and originating from incredible sources. Such data is untrustworthy, which is why data swamps cause more harm to organizations than good.

Data swamps are practically useless since data scientists and analysts cannot utilize these for analytics tools, visualizations, or artificial intelligence—inaccurate data yields unreliable results and insights. Any output from a data swamp would steer the enterprise in the wrong direction. Additionally, since a monetary cost is associated with building a data lake, data swamps result in financial loss.

Read more: Turning Your Data Lake Into a Data Swamp

Best Practices to Build a Data Lake & Overcome Challenges

A data lake design should follow the correct practices to ensure productivity and positive outcomes. Some of these practices are discussed below.

Data Governance

Before venturing into the big data space, work on correcting your internal workflows and pipelines. Organizations can do this by identifying data ingestion points and optimizing and restructuring the SQL queries for a better ETL pipeline. Restructuring internal workflows would allow you to collect all relevant information, e.g., metadata, and maintain an even data structure throughout the organization. The same principles can be applied to touchpoints collecting unstructured and semi-structured data.

Organizations can also work on identifying and eliminating data silos to improve data integration and quality.


Data privacy is essential for organizations dealing with general customer data, healthcare, or financial records. Before dumping such records into a data lake, ensure all necessary details are deidentified or skipped.


To avoid cyber mishaps, work on building an infrastructure for data security. This may require hiring trained cyber security specialists and increased costs from cloud security tools, but the investment is worthwhile. While providing access rights to the lake, ensure that the concerned party only has access to the required data.


DevOps procedures help establish reliable workflows for a data lake. Defining what data is to be placed in the lake and through what channel is vital. This creates robust pipelines that carry accurate, trustworthy and complete information and ensure these metrics are upheld in the future.


Enterprise data is collected so fast that carrying out manual dump operations is not feasible. Organizations should work to automate the ingestion process such that new data gets automatically placed within the defined structure. To make this possible, data lakes should be designed with appropriate directory structures to place all relevant data within the same group.


A data lake is a data platform that contains data for multiple use cases. Most of this data can be cleaned and used, but AI and ML have specific requirements. Data needs to be collected in formats and structures which machine learning engineers can utilize for artificial intelligence applications.

Related Reading: How to Make a Data Lake Worthwhile For Your Business

Is Data Virtualization Good for Building Robust Data Lakes?

Data virtualization (DV) allows for a logical data lake implementation without having to move or replicate the data. DV provides a virtual integration layer for all data sources across the system where users can interact and query their required information. Like data lakes, engineers can implement data virtualization locally or on cloud services such as Azure and AWS.

Data virtualization is simpler to implement since it avoids many of the hassles of a data lake, but it has many disadvantages. It is not scalable as with growing data; a virtual integration layer is not efficient for data management. Data lakes come with the added benefit of data protection due to data node replication, and specialized open-source tools such as Hadoop offer faster processing.

Boost Your Data Lake With offers data integration solutions that help speed up the data lake building procedure. Our scalable data ingestion pipeline allows users to connect with hundreds of data sources with a few clicks. This ensures that your data lake foundations are built on robust, future-proof channels.

Integrate offers easy connectivity to local database systems and cloud services such as AWS S3 or Google Cloud Storage. This means your data lake will be unhindered no matter where your data resides.

Book a 1:1 session with our experts today for professional consultation on data integration solutions.