Enterprise data lake: solution for scalable data analytics

Enterprises are inundated with vast amounts of data. According to IDC, the global datasphere will grow to 175 zettabytes by 2025, up from 33 zettabytes in 2018. Yet, despite this exponential growth, only 32% of data available to enterprises is fully leveraged, and 73% of data within organizations goes unused for analytics, leading to missed opportunities and inefficiencies.

Let's explore the concept of an enterprise data lake-a scalable and flexible data storage solution. An enterprise data lake consolidates all types of data into a single repository. Data lake consulting enables organizations to break down data silos and enhance accessibility for advanced analytics and Business Intelligence.

In this blog post, we will discover how your enterprise can leverage a well-implemented enterprise data lake strategy based on our experience developing a data lake strategy for one of the biggest telecom providers in Europe.

What is an enterprise data lake?

Enterprise data lakes store data in its raw form at the time of ingestion, avoiding the need for time-consuming and costly ETL (extract, transform, load) processes. Structured data, such as relational databases; semi-structured data, like JSON and XML files; and unstructured data, including text, images, and videos, can all be stored in their raw form.

Additionally, they offer robust data preparation services, which include profiling, cleansing, enriching, transforming, and modeling data to meet specific application requirements. This approach ensures that data quality is maintained and analytics and Business Intelligence applications can be effectively supported. Here are some common pain points that highlight the need for an enterprise data lake:

• If your organization struggles with data silos, making it difficult to access and integrate data from different departments;
• Whether you're dealing with outdated legacy systems that cannot keep up with modern data demands,
• If data quality and consistency issues are hampering your analytics efforts,
• When real-time data processing is critical, but your existing infrastructure only supports batch processing,
• If your business needs to support advanced analytics and Machine Learning yet lacks the infrastructure to handle large, diverse data sets,
• If your existing data infrastructure cannot keep up with the growing data volume and complexity.

Read more: Data lake vs data warehouse: Which one to choose for your business?

Enterprise data lake architecture

A well-architected enterprise data lake integrates several key components to ensure efficient data management and analysis:

• Data ingestion involves collecting and importing data from multiple sources into the data lake, supporting both batch and real-time ingestion.
• Data storage utilizes scalable, durable, and cost-effective solutions that handle large volumes of structured, semi-structured, and unstructured data.
• Data processing transforms raw data into usable formats through batch, stream, and ETL/ELT processes.
• Data cataloging organizes and manages metadata, making data easily discoverable. It facilitates metadata management and provides a searchable inventory of data assets.
• Data integration combines data from various sources while orchestration coordinates data workflows. Data engineers manage integration and automate workflows.
• Data access and query engines enable efficient data querying, providing SQL-based access to data.

In addition to these components, a data lake architecture should provide mechanisms for implementing data governance policies, including metadata management, data lineage, role-based access controls, etc.

Find out more about: Enterprise data warehouse: From raw data to unified analytics

How enterprise data hub can improve operational efficiency: 7 key use cases

All-in-one customer view

A customer 360-degree view involves aggregating data from various sources, such as CRM systems, social media, transactional data, customer feedback, and IoT devices, into the data lake. After integrating and cleaning data through ETL processes, enterprises can perform advanced analytics and ML to understand customer behaviors, preferences, and trends.

This comprehensive view enables personalized marketing, improves customer satisfaction, and enhances retention. For example, a retail company can merge online and in-store purchase data with social media interactions. They can identify customer preferences and tailor marketing campaigns, increasing sales.

Fraud detection and prevention activities

Fraud detection and prevention entail real-time ingestion of transactional data into the data lake from sources like transactional systems, log files, credit scores, behavioral data, and external fraud databases. Machine Learning models are applied to detect anomalies and potential fraud patterns when continuously monitoring and analyzing different transactions.

This approach helps reduce financial losses due to fraud, enhance security measures, and build customer trust. The bank can analyze millions of transactions per second, applying algorithms to detect and flag potentially fraudulent activities for immediate review.

Predictive equipment failures and maintenance

Predictive maintenance involves ingesting real-time sensor data from machinery, historical maintenance records, and operational logs into the data lake. With implemented predictive analytics and ML, organizations can identify patterns that indicate potential equipment failures. This enables timely maintenance, reducing unplanned downtime and extending the equipment's lifespan.

For example, a manufacturing company can monitor machinery in real-time, using predictive models to schedule maintenance before breakdowns occur, thus saving significant costs.

Risk management practices

Risk management is about ingesting data from financial records, market data, regulatory reports, and internal audit findings into the data lake. Data engineers should apply risk assessment models and analytics so organizations can identify, analyze, and mitigate risks effectively. The innovative financial institution can use its enterprise data lake to monitor and analyze market trends and regulatory changes, ensuring compliance and mitigating financial risks.

Archiving historical data

Besides data lakes allow organizations to archive vast historical data in its raw format. This is particularly useful for data that might not have an immediate purpose but could be valuable for future analysis.

Over time, this archived data can be mined to identify long-term trends, patterns, and anomalies that can inform strategic decisions. When the data is stored in its original form, data lakes preserve the integrity and richness of the information, making it possible to reanalyze it with new tools and techniques as they become available.

Supporting experimental analysis

Data lakes enable experimental analysis of raw data. If the data gets corrupted, the original raw data can always be retrieved for further experiments. This capability is invaluable for scientific research, healthcare, and R&D departments that require extensive data manipulation and hypothesis testing.

For example, pharmaceutical companies can use data lakes to store raw clinical trial data, allowing researchers to test various hypotheses and models without losing the original data. Researchers can experiment freely, knowing they can always return to the untouched data.

Discover more: Enterprise data hub: architecture and use cases

N-iX success story: Developing an enterprise data lake strategy

Lebara, one of the growing mobile virtual network operators in Europe, needed to revamp its legacy IT infrastructure to support rapid growth and expansion into new markets. The primary challenge was integrating disparate data systems and ensuring seamless data flow. All this must have been completed when maintaining high data quality and compliance.

N-iX established a dedicated R&D center to support Lebara's digital transformation. The team focused on building a scalable data lake solution. This involved migrating data from on-premises systems to the cloud, developing a microservices architecture, and implementing automated data ingestion and processing pipelines.

How we approached the data lake development:

1. N-iX utilized advanced ETL tools to automate the data ingestion process, integrating data from various sources such as transactional systems, customer databases, and IoT devices;
2. The team migrated data infrastructure from on-premises to a multi-cloud environment, utilizing AWS and Azure. This strategy provided flexibility and reduced vendor lock-in risks. The data lake was built on cloud-native storage solutions such as Amazon S3 and Azure Data Lake Storage;
3. N-iX implemented Apache Spark for distributed data processing, allowing for efficient handling of large-scale data transformations and analytics;
4. We integrated metadata management tools like Apache Atlas. This provided comprehensive data lineage tracking, facilitating data governance and enhancing data transparency. Role-based access controls and data encryption were implemented to secure sensitive information.

The enterprise benefited from our services:

• The data lake enabled seamless integration of diverse data sources, breaking down data silos and improving data accessibility;
• Advanced analytics and Machine Learning capabilities were supported, providing deeper insights into customer behavior;
• Automated data pipelines and real-time data processing reduced the time required to introduce new features and services;
• Comprehensive data governance and metadata management ensured high data quality and compliance with regulatory requirements.

Read more about: Faster time-to-market with full-scale digital transformation in telecom 

Challenges and mitigation strategies in building an enterprise data lake

Below are some common challenges organizations face when designing a data lake for enterprises. At N-iX, we apply reliable data strategies to address them.

Data ingestion and integration

Integrating diverse data sources with different formats, structures, and update frequencies can be complex. Managing real-time and batch data ingestion simultaneously adds to this complexity. Inconsistent data ingestion pipelines can lead to data quality issues and delays in data availability.

N-iX approach: We adopt a robust data ingestion framework for batch and real-time processing. Tools like Apache NiFi, Wavefront, Funnel, Amazon Kinesis, and Kafka can handle diverse data sources and provide scalable ingestion pipelines. We implement standardized data ingestion processes to ensure consistency and reliability.

Data governance and security

Data governance and security in a data lake environment are complex due to data volume, variety, and velocity. Regardless of the company's size, ensuring data privacy, compliance with regulations, and protecting against unauthorized access are critical concerns.

N-iX approach: We develop a comprehensive data governance framework that includes policies for data ownership, access control, and data lifecycle management. Also, it's a necessity for us to implement encryption for data at rest and in transit, role-based access control (RBAC) and data masking to protect sensitive data of our clients.

Metadata management

Managing metadata across diverse data sources and formats could be difficult. Without proper metadata management, a data lake can quickly become a data swamp, making it easier to find and use data.

N-iX approach: We implement robust metadata management tools to track data lineage, understand data provenance, and ensure data consistency. Following the best data practices, we establish processes for regularly updating and maintaining metadata to reflect any changes in data sources and structures.

Data lake management and maintenance

Managing and maintaining a data lake involves continuous monitoring, updating, and troubleshooting. Ensuring the data lake operates smoothly and meets business needs requires significant effort and expertise.

N-iX approach: Our data experts set up automated alerts for critical events and performance issues to enable proactive troubleshooting and regularly review and update data lake configurations to align with evolving business requirements.

Accelerating data transformation to keep pace

For a data lake to be effective, it must support data democratization, reuse, exploration, and analysis. This requires a robust process of data ingestion, extraction, cleaning, integration, data set discovery, versioning, and metadata administration. Without proper implementation and management, data lakes can produce unreliable outcomes.

Partner with expert guidance and support to overcome integration challenges. At N-iX, we specialize in designing, implementing, and optimizing data lake solutions for enterprises. Contact N-iX to drive your business forward with data-driven insights and innovation.

Why talk to N-iX for enterprise data lake consulting services?

• With over 60 successful data projects delivered, N-iX offers expertise in implementing data solutions;
• Our data practice includes more than 200 experts who can assist with data lake consulting needs;
• N-iX has over 400 cloud leaders and official partnerships with Google Cloud, AWS, and Microsoft Azure;
• N-iX has received numerous industry recognitions, including being selected as a "Rising Star in Data Engineering" by ISG and earning a spot in the Global Outsourcing 100;
• We prioritize data protection by adhering to established service quality and privacy policies such as GDPR, PCI DSS, ISO 9001:2015, and ISO 27001:2013.