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Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

Last updated on June 5, 2024

In today’s data-driven world, organizations face enormous challenges as data grows exponentially. One of them is data storage.

Traditional data storage methods in analytical systems are expensive and can result in vendor lock-in. This is where data lakes come to store massive volumes of data at a fraction of the expense of typical databases or data warehouses.

Adopting a data lake approach enables businesses to easily manage their enormous data vaults, keeping them competitive in an increasingly data-centric environment.

But where do you get started in data lake implementation? Keep reading to learn all the essentials about data lakes and jump-start your data strategy using this incredibly powerful solution.

What is a Data Lake?

Data Lake Features

A data lake is a centralized repository designed to store massive amounts of data in its natural, raw format, whether structured, semi-structured, or unstructured. The versatility of data lakes makes it easier to meet changing data types and analytical requirements in an organization. 

A data lake uses a flat design rather than typical hierarchical structures and predetermined schemas like data warehouses. This structure is made more efficient using data engineering approaches such as object storage, providing metadata tagging, incorporating unique identifiers, simplifying data retrieval, and improving overall performance.

If the complexities of big data are becoming too much for your current systems to handle, a data lake might be the solution you need.

Data Lake vs. Data Warehouse vs. Cloud Data Lakes

How do data lakes vary from data warehouses? And when should you use which one?

While data lakes and data warehouses share the ability to store and analyze data, they have distinct specializations and use cases. That is why an enterprise-level organization’s analytics ecosystem often includes a data lake and a data warehouse. Both repositories collaborate to provide a secure, end-to-end system for storage, processing, and faster access to insights.

A data lake collects relational and non-relational data from various sources, including business applications, mobile apps, IoT devices, social media, and streaming, without the need to specify the data’s structure or schema until it’s read. 

Schema-on-read assures that all data types may be saved in their original form. As a result, data lakes may accommodate a wide range of data types, from structured to semi-structured to unstructured, of any size. The adaptability and scalability of data lakes make them crucial for extensive data analysis using various compute processing tools, such as Apache Spark or Azure Machine Learning.

In contrast, a data warehouse has a relational structure. The structure or schema is modeled or specified by business and product needs, which are then vetted, conformed, and optimized for SQL query operations. 

A data lake contains data of various structural kinds, including raw and unprocessed data, whereas a data warehouse stores data that has been handled and converted for a specific purpose and may subsequently be used to provide analytic or operational reports. This makes data warehouses suitable for delivering more uniform kinds of BI analysis and supporting a pre-defined corporate use case.

Data Lake Data Warehouse
Data type supported Structured, semi-structured, unstructured
Relational, non-relational
Schema Schema on read Schema on write
Scalability Easy to scale at a low cost Challenging to scale, generates high costs
Data format Raw Processed
Example use case Real-time analytics, predictive analytics, machine learning Business Intelligence (BI)

Cloud vs. On-Premises Data Lakes

Most organizations have typically used data lakes in their on-premises data centers. However, modern data lakes often run in cloud architectures.

The development of big data cloud platforms and numerous managed services using tools like Spark and Hadoop sped up the cloud transition. Leading cloud providers such as Google, Microsoft, and AWS now provide technology stacks for big data analytics applications.

Another element driving the expanding cloud data lake trend is the growth of cloud-based object storage systems such as Google Cloud Storage, Azure Blob Storage, and Amazon S3. These services provide an alternative to data storage solutions such as Hadoop Distributed File System (HDFS). All in all, cloud solutions often help to reduce data storage costs, so they’re definitely worth considering.

Data Lake Architecture

This section explores data architecture using a data lake as the central repository. While we focus on the essential components, such as the ingestion, storage, processing, and consumption layers, it’s crucial to highlight that current data stacks may be constructed in various architectural styles. 

Both storage and compute resources can be deployed on-premises, in the cloud, or in a hybrid arrangement, providing several design options. Understanding these essential layers and how they interact will allow you to design an architecture tailored to your organization’s needs.

Data Sources

Data sources may be roughly divided into three groups:

  • Structured Data Sources – These are the most organized kinds of data, typically derived from relational databases and tables with well-defined structures. SQL databases such as MySQL, Oracle, and Microsoft SQL Server are common structured data sources.
  • Semi-Structured Data Sources – This form of data is somewhat organized, although it does not fit neatly into tabular frameworks. Examples include HTML, XML, and JSON files. While they may contain hierarchical or tagged structures, they require additional processing to properly organized.
  • Unstructured Data Sources – This category comprises a wide range of data types that lack a predetermined structure. Unstructured data can include sensor data in industrial Internet of Things (IoT) applications, videos and audio streams, photos, and social media information such as tweets and Facebook postings.

Understanding the data source type is critical because it impacts later processes in the data lake pipeline, such as data input techniques and processing needs.

Data Ingestion

Data ingestion is the process of bringing data into a data lake from numerous sources. It acts as a gateway for data entering the lake, either in batch or in real time, before being processed further.

Batch ingestion is a way of importing data that is planned at regular intervals. For example, it may be configured to run nightly or monthly, sending significant amounts of data at a time. Apache NiFi, Flume, and classic ETL technologies such as Talend and Microsoft SSIS are commonly used for batch ingestion.

Real-time ingestion transfers data into the data lake as it is created. This is critical for time-sensitive applications such as fraud detection and real-time analytics. Apache Kafka and AWS Kinesis are prominent solutions for managing real-time data input.

The ingestion layer, which combines batch and streaming data processing capabilities, frequently uses numerous protocols, APIs, or connection techniques to communicate with the many internal and external data sources we covered previously. The multiplicity of protocols guarantees a seamless data flow while accommodating the varied character of the data sources.

In the context of a data lake, the ELT paradigm often initiates post-ingestion modification. This strategy initially loads data from the source into the data lake’s raw or landing zone. Lightweight transformations may be used; however, the data is frequently left in its original format. The data is then transformed into a more analyzable format within the data lake.

Data Storage And Processing

The data storage and processing layer is where the ingested data is stored and transformed to be more accessible and useful for analysis. This layer is typically separated into zones for ease of administration and workflow efficiency.

Raw Data Storage 

The raw or landing zone is where ingested data first arrives. At this point, the data is in its natural format—structured, semi-structured, or unstructured. The raw data store serves as a depository for data before it is cleaned or transformed. This zone uses storage systems such as Hadoop HDFS, Amazon S3, and Azure Blob Storage.

Transformation Part

Data goes through several changes after it is in the raw zone. part portion is quite flexible, providing batch and stream processing.

Here are a few transformations that take place at this layer:

  • Data Cleansing – it consists of deleting or correcting erroneous records, errors, or inconsistencies in the data
  • Data Enrichment – it enhances the original data collection by including additional information or context
  • Data Normalization – converts data to a standard format, providing consistency
  • Data Structuring – includes converting unstructured or semi-structured data into a structured format appropriate for analysis

Following these modifications, the data is sometimes referred to as trustworthy data. It’s more dependable, clean, and appropriate for various analytics and machine learning models.

Processed Data

After transformation, the data can be relocated to a new zone known as the refined or conformed data zone. Additional transformation and structuring may be required to prepare the data for specific commercial use cases. 

Analysts and data scientists generally work with this type of data. It’s more accessible and simple to use, making it excellent for analytics, business intelligence, and machine learning projects. You can use tools like Dremio or Presto to query this enhanced data.

Analytical Sandboxes

Analytical sandboxes are separated settings for data exploration, enabling tasks like discovery, machine learning, predictive modeling, and exploratory data analysis. These sandboxes are purposefully kept distinct from the major data storage and transformation layers to guarantee that experimental activities do not jeopardize the integrity or quality of data in other zones.

Both raw and processed data may be fed into the sandboxes. Raw data might benefit exploratory tasks when the original context is important, but processed data is commonly utilized for more sophisticated analytics and machine learning models.

Data discovery is the first phase, in which analysts and data scientists examine the data to determine its organization, quality, and potential worth. This frequently includes descriptive statistics and data visualization.

Machine learning and predictive modeling can be the next steps here. After a thorough grasp of the data, you can use ML techniques to develop prediction or categorization models. This phase may utilize a variety of machine learning libraries, such as TensorFlow, PyTorch, or Scikit-learn.

Exploratory data analysis (EDA) is another option. EDA uses statistical visualizations, charts, and information tables to analyze data and comprehend the links, patterns, or anomalies between variables without making assumptions.

Jupyter Notebooks, RStudio, and specialist tools like Dataiku or Knime are frequently used in these sandboxes to create workflows and conduct studies. The sandbox environment allows you to test ideas and models without disrupting the primary data flow, fostering a culture of experimentation and agile analytics inside data-driven enterprises.

Data Consumption

In this layer, all the results of all previous efforts are realized. The cleaned, dependable data is now available for end users and may be accessed using Business Intelligence tools like Tableau or PowerBI. 

It also serves as a hub for specialist positions, such as data analysts, business analysts, and decision-makers, who use processed data to drive business choices.

Data Lake Implementation Principles

Preserve the Data Systematically

Retain all of your data, as business insights can emerge from any subset of that data at any point. They originate from innovative business analysts and data scientists who can connect the dots across datasets and time periods. Such inventiveness is impossible unless the data has been kept systematically.

Raw Data Has Business Value

All of your data, including raw, dirty data, contains intrinsic commercial value that is simply waiting to be discovered. Simply owning certain company data may provide you with new business prospects in the future.

Workflows in a data lake should guarantee that no data is destroyed because of current business or engineering thinking. At the same time, it should not devolve into a disorganized data swamp in which data is dumped without context.

Support Diverse Users and Roles

Your data lake should serve all of your company’s data responsibilities and use cases:

  • Business analysts are looking for business intelligence data in relational databases
  • Data scientists demand raw data formats such as JSON and CSV, as well as relational databases
  • Data engineers anticipate data in binary formats like Parquet to improve performance

Provide Self-Service Features to All End Users

A data lake should offer self-service options to all of its end users. Your team members may search for the required data, request and obtain access automatically, examine the data, do analytics, and upload any new data or findings to the data lake.

Traditionally, databases and data warehouses required an IT department to assist analysts and data scientists. Such an approach is just not feasible for the size of a data lake.

Data Lake Components

Raw Data Layer

The raw data layer, also known as the intake layer, is the initial checkpoint where data enters the data lake. This layer accepts raw data from various external sources, including IoT devices, data streaming devices, social media sites, wearable devices, and more.

It supports a wide range of data formats, including video feeds, telemetry data, geolocation data, and even data from health monitoring devices. Depending on the source and demand, this data is ingested in real time or in batches and preserved in its original format with no adjustments or modifications. The ingested material is then arranged into logical folders to facilitate navigation and accessibility.

Standardized Data Layer

While optional in certain implementations, the standardized data layer becomes increasingly important as your data lake grows in size and complexity. This layer serves as a bridge between the raw and curated data layers, improving the data transfer performance between them.

The raw data from the intake layer is formatted here, transforming it into a standardized form that may be processed and cleansed further. This transformation involves altering the data structure, encoding, and file formats to improve the performance of future layers.

Cleansed Data Layer

As we get deeper into the architecture, we come across the cleaned data or curated layer. This is where the data is converted into usable datasets that are ready for analysis and insight development. The layer conducts data processing operations such as cleaning, normalization, and object consolidation.

The resulting data is saved in files or tables, making it available and ready for use. This layer standardizes data in format, encoding, and data type to ensure consistency across the board.

Data Version Control Layer

Data version control ensures that every change to the data is tracked, enabling teams to reproduce past results and understand the lineage of data transformations. This is critical in a data lake where data comes from diverse sources and undergoes multiple transformations.

By versioning datasets, teams can reproduce experiments and analyses exactly, which is crucial for collaborative work and auditing. That’s how versioning enables multiple users to work on data collaboratively without worrying about conflicts: changes can be tracked, merged, and reverted as needed.

Data versioning also provides a clear lineage of data transformations, helping to understand the origin of each dataset version, what transformations were applied, and who made changes. This helps a lot with debugging and compliance.

Application Data Layer

The application data layer, also known as the trusted layer, adds business logic to the previously cleaned and curated data. It guarantees that the data precisely corresponds with business needs and is suitable for distribution across several applications.

Surrogate keys and row-level security are two specific approaches used here to further protect data. This layer also prepares data for your organization’s machine learning models and AI applications.

Sandbox Data Layer

Finally, the sandbox data layer, which is optional but extremely useful, acts as an experimental playground for data scientists and analysts. This layer provides a controlled environment where advanced analysts may study data, spot patterns, test ideas, and gain insights.

Analysts can safely experiment with data enrichment from new sources while ensuring that the primary data lake is unaffected.

12 Steps Checklist for Data Lake Implementation

  1. Define Objectives and Scope – Identify business objectives and the data’s scope and magnitude.
  2. Assess Data Sources – Analyze internal and external data sources, including their forms and quality.
  3. Design Data Lake Architecture – Choose an appropriate platform that ensures scalability, security, and compliance, such as AWS, Azure, or Google Cloud.
  4. Data Governance and Compliance – Implement data governance rules and maintain compliance with legislation such as GDPR and HIPAA.
  5. Data Ingestion and Storage – Implement batch and real-time data intake technologies and divide data storage into raw, curated, and consumption zones.
  6. Metadata Management – Implement metadata management strategies to help with data categorization and discovery.
  7. Data Processing and Transformation – Create data processing frameworks and create ETL procedures.
  8. Data Quality and Integration – Perform data quality checks and integrate several data sources.
  9. Security and Access Control – Implement strong security measures and establish access control procedures.
  10. User Training and Adoption – Educate end users and stakeholders to encourage organizational-wide adoption.
  11. Monitoring and Maintenance – Set up performance and consumption monitoring and regular maintenance of the data lake.
  12. Continuous Evaluation and Improvement – Evaluate the data lake regularly and enhance it iteratively in response to feedback and changing demands.

Challenges in Data Lake Implementation

Data Volume and Diversity

Data’s sheer volume and diversity provide a substantial obstacle to data lake adoption. Managing various data kinds, from structured to unstructured, calls for strong systems capable of handling such diversity without sacrificing efficiency.

Integration and Architecture Complexity

Integrating a data lake into an IT infrastructure necessitates a multifaceted architectural strategy. This complexity derives from the requirement to assure compatibility with various data formats and sources, as well as existing data systems and procedures.

Data Ingestion and Processing

Data ingestion, or the act of integrating data into a data lake, may be difficult owing to the diversity of data sources and formats. Furthermore, digesting this data to obtain useful insights necessitates using advanced analytics techniques and technology.

Data Accessibility and Usability

It’s critical to ensure that the lake’s data is freely accessible and useful to all parties. This includes creating user-friendly interfaces and query languages and ensuring that data is properly structured and cataloged.

Data Quality and Consistency

Maintaining excellent data quality and consistency is critical. This entails developing methods to clean, validate, and standardize data as it enters the lake to guarantee that it is trustworthy and useful for analysis.

Security and Privacy Concerns

Data lakes frequently include sensitive information, so security and privacy are major priorities. Strong security measures, including access limits, encryption, and frequent security audits, are required to defend against data breaches and maintain compliance with privacy rules.

Cost Management and Optimization

Managing the expenses of storing and processing enormous amounts of data is a considerable problem. This covers direct expenses for storage and computer resources and indirect costs for administration and maintenance.

Technical Expertise and Resource Allocation

Specialized technological competence is required for the successful development of a data lake. Organizations must either train existing employees or acquire new personnel with the necessary abilities to handle and analyze big data efficiently.

Continuous Monitoring and Optimization

Continuous performance monitoring of the data lake, as well as continuous process and technology optimization, are required to guarantee that it meets the company’s demands and runs efficiently.

Popular Data Lake Implementation Technologies

Regarding data lake design, it is critical to consider the platforms on which these data lakes are constructed. Here are some of the top options in the sector, each with strong data lake offerings.

Amazon Web Services

Amazon Web Services (AWS) provides a strong data lake architecture built on the highly available and low-latency Amazon S3 storage solution. S3 is especially appealing to companies wishing to use AWS’s vast ecosystem, which includes supplementary services such as Amazon Aurora for relational databases.

AWS Amazon Web Services

One of S3’s key advantages is its easy connectivity with other AWS services. AWS Glue enables sophisticated data categorization, whereas Amazon Athena allows for ad hoc querying. Amazon Redshift is the preferred data warehousing option inside the ecosystem. This well-integrated collection of services simplifies data lake administration, but it can be complicated and may require specialist knowledge for optimal navigation.

AWS offers a rich but complicated collection of tools and services for creating and maintaining data lakes, making it an adaptable option for enterprises with various goals and ability levels.

Microsoft Azure

Azure Data Lake Storage (ADLS) is Microsoft Azure’s feature-rich data lake solution, developed primarily for companies who have invested in or are interested in Azure services. Unlike a distinct service, the recently announced Data Lake Storage Gen2 is an addition to Azure Blob Storage, providing a spectrum of data management features.

Microsoft Azure Data Lake Storage (ADLS)

The platform has built-in data encryption, which allows enterprises to safeguard their data at rest. Furthermore, it provides granular access control policies and complete auditing capabilities required to fulfill strict security and compliance standards.

ADLS supports Azure Private Link, a technology that provides safe and private access to data lakes over a private network connection. It also effortlessly interacts with operational stores and data warehouses, enabling a unified data management approach. The platform can manage heavy workloads, allowing users to do complex analytics and store massive amounts of data.

Google Cloud Platform

Google has several data lake offerings: 

  • Cloud Storage stores data as ‘blobs’ (binary big objects) within a ‘bucket.’ Blobs, such as files, videos, or operating system images, may only be acted on as a whole. For example, you cannot receive the file’s first page; you can only get the full file. Cloud storage uses a distributed storage engine, allowing you to grow to petabytes of data quickly.
  • BigQuery stores data in column-oriented database tables, with characteristics separated by columns. BigQuery uses decoupled storage and computation architecture, allowing you to scale to petabytes of data effortlessly.
  • Cloud SQL stores data in row-oriented database tables with characteristics separated by columns. Cloud SQL doesn’t work with a distributed storage engine; therefore scaling up becomes difficult once you reach 1 TB.
Google Cloud Platform (GCP)


Unlike typical data lakes, Snowflake refers to itself as a data cloud, breaking down data silos and allowing for seamless integration of structured, semi-structured, and unstructured data. The platform is noted for its speed and dependability, powered by an elastic processing engine that avoids concurrency difficulties and resource contention.

Snowflake platform

Snowflake’s success is attributed to its emphasis on flexibility and simplicity; data professionals frequently refer to it as a platform that “just works.” 

It has complex features such as Snowpark and Snowpipe, which enable multilingual programming and data streaming. Its efficient storage features include automated micro-partitioning, rest and transit encryption, and interoperability with cloud object storage, eliminating data migration.

On-Premises Technologies

In an On-Premises data lake scenario, data is ingested from numerous sources and stored in a distributed file system like Hadoop Distributed File System (HDFS) or Apache Parquet. The data is preserved in its original format, providing greater freedom in data exploration and analysis.

Data lakes can use technologies such as Apache Hive, Apache Spark, and Presto to facilitate data access and processing. These technologies provide the tools and frameworks for transforming and querying data stored in the data lake.

Data Lake Implementation With lakeFS

The data lake concept solves numerous challenges, such as storing data at scale, storing both structured and unstructured data in one location, and running analytics and machine learning on massive datasets. 

However, teams are still facing some major data management gaps in data lakes that would effectively prevent them from becoming data swamps.

This is where the open source solution lakeFS can help. It lets you create repeatable, atomic, and versioned data lake activities. With branching and committing architecture similar to Git, lakeFS allows for all changes to be made in isolation. 

Creating a branch allows you to alter production data, providing you with an isolated snapshot in which you may experiment without compromising actual production data. It also gives distinct versions of the same data across time and allows you to time travel between them. You can also read from the data lake at any moment, compare changes, and securely revert if required.

Data engineers benefit from lakeFS, particularly when importing new data or conducting validation procedures. Instead of ingesting data immediately into the main branch, it can be absorbed in an isolated branch first. This opens the door to crying out validation (data format and schema enforcement) before adding new data to the data lake. Adding data to an isolated branch can also lessen data corruption in production due to upstream changes or recently deployed code updates. 


Data lakes are an indispensable tool in a modern data strategy. They allow teams to store data in a variety of formats, including structured, semi-structured, and unstructured data – all vendor-neutral forms, which eliminates the danger of vendor lock-in and gives users more control over the data. They also make data easier to access and retrieve, opening the door to a wider choice of analytical tools and applications.

Want to learn more about implementing modern data lakes? Take a look at this guide: Building A Data Lake For The GenAI And ML Era.

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