Top 10 Leading Data Lake Tools in 2024: Choose the Right One

Are you looking for a data lake tool that is scalable, cost-efficient, and accessible, can store your business’s historical data, and can help you perform intelligent analytics? 

No worries. To lift the weight off your shoulders, I have compiled a list of data lake tools. This list will help you understand each tool’s key features and benefits so you can choose the right tool for your organization. 

Before we begin, here are the key takeaways of the data lake market:

• As of 2023, the Data Lake Market Size is valued at $15.2 Billion; 
• Its CAGR is expected to increase by 20.5% from 2024 to 2032 (each year), and 
• Its market value is evaluated at $80.2 Bn by 2032. 

Now, without further ado, let’s dive right in!

What is a Data Lake?

A data lake is an adaptable centralized storage repository. You can store large amounts of raw business data in structured, semi-structured, and unstructured formats. Using data lakes is time-saving and flexible.

Modern data lakes provide solutions like ingestion, storage, processing, governance, and analytics. They let organizations choose the data lakes according to their needs, making them more scalable, efficient, and cost-effective.

According to the Data Lake Market Analysis Report, the solution segment has made more than 78% as of 2023. That being said, 20% growth is expected each year from 2024 to 2032. 

Benefits of Using a Data Lake

Unlike the traditional data storage spaces, data lakes have the following benefits:

• Support for varied data formats: A data lake stores different data formats. These can be structured, semi-structured, and unstructured, like texts, images, and videos, all in one place. Thus, you can have a comprehensive view of all your data.
• Structureless data storage: You can store data in its original format. You do not have to organize or structure the data before storing it in a data lake. This saves time and allows for more flexible data use later.
• Scalable and cost-effective: Your data lakes can grow and store massive amounts of data without becoming too expensive. This is possible because of distributed computing and cloud storage.
• Efficient data processing: You can use data lake tools like Snowflake and Hadoop. This stores and processes data faster, even when dealing with large volumes.
• Easy insight extraction: Your data users can combine information from many datasets, which facilitates data exploration and better decision-making.
• Ensures data accessibility: Data lakes provide manageable and accessible storage space. Your data users can access and use data without relying on IT departments.

List of 10 Best Data Lake Tools in 2024

Data lake tools help enhance your data storage and analytics capabilities. So you can make the most of your time and resources. Below listed are the best tools present in the market:

1. Amazon Simple Storage Service (S3)
2. Dremio Lakehouse Platform
3. Azure Data Lake Storage
4. Databricks Delta Lake
5. Google Cloud Platform (GCP)
6. Snowflake
7. Starburst Data Lakehouse
8. Apache Hadoop
9. Apache Hive
10. Apache Spark

1. Amazon Simple Storage Service (S3)

G2 Rating: 4.6/5.0

S3 is a cloud-based tool that lets you break down siloed data and increase data insights. With S3, you can also use AWS analytics services. These services support all your data needs, from integration to big data analytics. This tool can form secure data lakes in a matter of days.

Key Features/Benefits

• Scalability: With S3, your data storage is possible up to exabytes, with excellent performance. As you add or remove your data, S3 adjusts its size. Advanced storage setup is not required; you pay for what you use.
• Durability and Availability: You get the most reliable and available cloud storage. S3 ensures your data is seldom lost with 99.999999999% durability. Also, your data availability is 99.99%. The best SLAs in the industry support it.  
• Security and Data Protection: Your data is secure and protected, and you have complete access control. S3 is private and encrypts your data by default. 

Use Cases

• Run Data Analytics: You can organize, process, and analyze data in your S3 data lake to gain valuable insights and enhance decision-making. 
• Apply Machine Learning: You can improve your decision-making by training and deploying ML models to make data-based decisions. 

2. Dremio Lakehouse Platform

G2 Rating: 4.6/5.0

Dremio is an open-source lakehouse platform. It allows the fastest SQL querying with Apache Iceberg. It lets you access, manage, and analyze all your data, both in cloud and on-premise.

Key Features/Benefits

• Unified Data View: Using this platform, you can bring data from different sources, such as relational databases, NoSQL databases, and cloud storage. Data access and usage become easy. 
• Easy to Use: It has a user-friendly interface. You can curate and share datasets without relying on IT.
• Support and Security: You get a large, active community providing 24/7 training and support. Also, this tool ensures data security and compliance with enterprise-grade features. 

Use Case

• Data Virtualization: You can access and analyze your data in real-time without the need for replication by using Dermio’s ability to virtualize the data present. 
• Data Lake Migration to Cloud: You can easily migrate your data lake from on-premise to the cloud using Dermio. It helps you accommodate more data with increasing demand flexibly. 

3. Azure Data Lake Storage

G2 Rating: 4.5/5.0

Azure Data Lake Storage (ADLS) is a cloud-based storage service. It is a single storage platform for eliminating siloed data. Your business gets an intelligent and scalable data environment for leveraging data to extract valuable insights. You also get the flexibility to create cost-effective cloud data lakes by adjusting storage and computing independently. 

Key Features/Benefits

• Scalable and Performant: This tool allows you to quickly scale and manage data up to petabytes, allowing high-performance analytics queries to run on a large scale. 
• Security: This tool has features like encryption, data access control, and network security. So you can gain insights from a secure data lake. 
• Compatibility: ADLS is compatible with Hadoop. You can organize and store your data in a detailed way using the tool’s folder system, which is the same as a traditional file system.

Use Cases

• Big-data Analytics: This tool is interoperable with Azure Data Factory, Azure Databricks, Azure HDInsight, and Power BI. Hence, you can ingest, process, and visualize huge amounts of data for analytics. 
• On-Cloud Storage: This tool provides reliable and scalable storage space for your data-heavy tasks.

4. Databricks Delta Lake

G2 Rating: 4.5/5.0

Delta Lake is an open-source software developed and actively updated by Databricks. It is an optimized storage layer that forms the foundation of your Databricks’ lakehouse. This tool gives you a solution to improve data lake reliability and enhance data quality. It is a great tool for your data analytics project. 

Key Features/Benefits

• ACID Transaction: This tool utilizes ACID transactions, which stand for atomicity, consistency, isolation, and durability. Your data is always consistent and reliable.
• Schema Enforcement and Evolution: Delta Lake maintains data quality by checking that your data matches the table schema. It also allows you to update the schema without stopping data operations.
• Robust Security: Delta Lake provides strong data security and integrity. It is ideal for your industry, which needs strict data governance and compliance.

Use Cases

• Multi-Cluster Writes: You may send multiple clusters of write operations to the same table concurrently, but your data won’t be corrupted. 
• Machine Learning: This tool supports favorable machine learning features, such as ACID transaction and schema enforcement. So you can use it to implement ML models. 

5. Google Cloud Platform (GCP)

G2 Rating: 4.5/5.0

GCP is a cloud-based data lake. Using Google Cloud’s data lake, you can analyze any type of data. It offers you large volumes of data ingestion, analysis, and storage on a single platform. 

Key Features/Benefits

• Scalability: You can scale and handle large volumes of data seamlessly using GCP. It meets all your organization’s needs without compromising on performance.
• Data Governance and Security: Your data quality and security are maintained with solid features like encryption, access control, lineage tracking, and audit logs.
• Cost-Efficient: GCP is an affordable choice as it has a flexible pricing model, which helps your business save money on data storage and processing. 

Use Cases

• Re-Host Data Lake: This platform offers lift and shift functionality, which lets you take your data lake from on-premise to the cloud without rebuilding it.
• ML and BI: GCP provides tools like TensorFlow, Cloud ML Engine, BigQuery, and Data Studio. This enables you to leverage advanced machine learning capabilities to gain valuable insights and make informed decisions.

6. Snowflake

G2 Rating: 4.5/5.0

Snowflake is a cloud data lake that is easy to deploy. It is a fully managed storage that provides you with security, back up, and optimized data files. It enables you to access external data by integrating with external storage.  

Key Features/Benefits

• Dynamic Architecture: Its architecture allows you to operate many computing clusters concurrently. Your team can run queries without interfering with each other.
• Scalable and Secure: You can scale your data up or down effortlessly. It offers high performance with zero failures. You can share the same data source between your organization or external parties. It ensures low-risk and better insights.
• Zero-Copy Cloning: This feature lets you make clones of the datasets without additional cost. This is useful for data engineers and analysts to run practice tests without changing the original dataset. 

Use Cases

• Storage and Analytics: This tool excels at managing large data volumes. That can include relational databases, NoSQL databases, and cloud storage. This enables enhanced analytics and improved decision-making.
• Machine Learning: Using Python libraries, you can run custom ML models. You can also scale features and models using Snowflake Feature Store and Model Registry. 

7. Starburst Data Lakehouse

G2 Rating: 4.5/5.0

Starburst is an open data lakehouse offering high-quality, reliable data with ACID transactions. You get one point for access and governance of all your data, ensuring performance and scalability remain high. 

Key Features/Benefits

• Open Data Lakehouse: You get table format and advanced warehouse features on your data lake. Also, you get transparency and flexibility as it is open-source. 
• Security and Compliance: It offers robust security features, like encryption, auditing, and complete access control, that keep your sensitive data secure. 
• Scalable and Cost-Effective:  You can scale data with the growing needs of your business seamlessly at cost-effective pricing options. This helps your business save money on data storage and processing.

Use Cases

• Advanced Analytics: As you make this lakehouse your storage space, your BI teams can use tools like Tableau to leverage historical, current, and real-time data for better decision-making.
• Improved Querying: You can query better at a reasonable cost, thanks to this tool’s columnar and read-optimized open table formats that support data skipping and partition handling.

8. Apache Hadoop

G2 Rating: 4.4/5.0

Aapache Hadoop is an open-source software, used to build data lakes. Your data is stored in HDFS (Hadoop Distributed File System) where it can be simultaneously processed. It is reliable, secure and you are allowed to have a distributed computing system. 

Key Features/Benefits

• Scalability: Because of the framework of Hadoop’s software library, you can scale up to thousands of servers for distributed processing of large datasets. Each server has its own local computation and storage space. 
• High availability and Fault Tolerance: Its library at the application layer detects and handles failures. You always get a highly available service, even with a cluster of computers that are prone to failures. 
• Cost-Efficient: Your cost of building large data sets is reduced by using Hadoop as it is open source. 

Use Cases

• Data Analysis: Hadoop can integrate with frameworks like Apache Spark and Apache Storm. This helps you to ingest, process, and analyze your data in real-time. 
• Big Data Exploration: Using Hadoop, data scientists in your organization can explore and discover patterns from large datasets. They can use ML algorithms and statistical models to do so. 

9. Apache Hive

G2 Rating: 4.2/5.0

Apache Hive is an open-source data lake tool that is an important component of many data lake architectures. It is built on top of Hadoop. Using HDFS it supports your data storage in Amazon S3, and ADLS. 

Key Features/Benefits

• Security and Monitoring: Your data is always secure, and you can monitor it when you use Apache Hive. This is because it supports Kerberos auth and integrates with frameworks like Apache Ranger and Apache Atlas.
• Backup and Recovery: For your data replication, this tool uses Bootstrap for the initial full backup, followed by incremental updates for the changes made since the last replication. Ensuring your data recovery is up-to-date. 
• SQL-Querying: You can read, write, and manage large amounts of data in its distributed storage system by querying using SQL language. 

Use Cases

• Data Analytics: Using Hive Metastore (HMS), you get a central metadata repository that can be analyzed to make informed and data-driven decisions. 
• BI and Reporting: You can apply complex querying on large datasets. Their outputs can be used to create business intelligence and for reporting purposes.  

10. Apache Spark

G2 Rating: 4.2/5.0

Apache Spark is an open-source data lake tool with over 2000 contributors from industry and academia. You can scale your data to thousands of machines, as this tool integrates with your favorable framework. 

Key Features/Benefits

• Spark SQL Engine: You can leverage your SQL knowledge. Spark SQL automatically adjusts its execution plan while running and works on structured tables and unstructured data like JSON. 
• Batch/Streaming Data: You can use your preferred language, Python, SQL, Scala, Java, or R, for unified processing of your data in batches or through real-time streaming. 
• Zero-cost: You can use this tool for free according to your business requirements, as it is open-source and has no licensing cost. 

Use Cases

• Machine Learning: You can train and test your machine learning models on one laptop and use the same code to scale thousands of other machines, making them fault-tolerant. 
• Data Analytics: You can perform EDA (exploratory data analysis) on petabytes of data. That means you do not have to break your data into smaller chunks. 

Tabular Comparison of Data Lake Tools

Steps for Selecting the Right Data Lake Tool 

Before selecting the right data lake tool for your business, make sure to follow the following steps: 

• Step 1: Identify your business needs: 
  • Get a clear understanding of why you need a data lake,
  • What will you use it for, and 
  • What type of data are you going to store? Is it structured, unstructured, or semi-structured? 
• Step 2: Assess how well the tool integrates data:
  • See if the tool integrates and works well with your existing data sources and 
  • Look at the way your data is integrated, transformed, and transferred. 
• Step 3: Look for the tool’s scalability and performance:
  • Make sure the tool you choose can handle massive data volumes for future growing needs with constant performance. 
• Step 4: Check for data governance and security: 
  • Ensure your data stays safe with features like encryption, authorization, and access control.
  • Your data lake tool should comply with relevant data regulations and support audit trails. 
  • Also, check for governance features like data cataloging, lineage tracking, and metadata management. 
• Step 5: Evaluate the total cost:
  • Compare different tools’ pricing plans and licensing costs and select the one that fits your budget. 
  • You should also add the hardware, maintenance costs, and training and consultation fees. 
• Step 6: Assess analytics and querying ability:
  • Make sure the tool supports different analytical techniques and 
  • Provides you with flexible querying options, like SQL-like languages. 
• Step 7: Understand the architecture: 
  • It is important to consider the tool’s underlying architecture and support for your on-premise, cloud, or hybrid needs. 
• Step 8: Understand support and community: 
  • Go for a tool that gives you regular updates and support, and 
  • Choose a tool with an active user community to get helpful resources. 

Future Trends of Data Lake Tools

With ever-changing and advancing technology, the future trends of data lake tools include: 

• Advanced analytics using ML and AI: In the future, your data lake tools will integrate more with AI and ML models, making it easier to gain valuable insights with advanced analytics.
• Enhanced user experience: Better interfaces and self-service abilities will be available, including querying using natural language. Your organization’s users can work with data without any technical expertise.
• Improved data handling: Your data lake architecture will improve in terms of integrating all data storage spaces. This would streamline the process of data management and reduce silos data. 

Summing Up

As we end this blog, you have a list of the best data lake tools available. I have also included the benefits of using these tools and explained how you can choose the right tool tailored to your needs. 

You can use Hevo to transfer your data from over 150 sources into your preferred data lake tool at an affordable price. 

FAQs

1. What is a data lake vs database?

• Data Lakes are flexible, scalable, and can store data in any format, whereas databases have a rigid schema structure and have limited scalability.
• Data lakes are used for big data analytics, whereas databases are used for data storage and accessing using structured tables. 

2. How do data lake tools support big data analytics?

Data lakes allow you to ingest large amounts of data in real-time. Their scalability and security enable you to leverage data with parallel processing and advanced analytics. Also, integrating machine learning and business intelligence tools makes comprehensive data analytics easy. 

3. Can data lake tools be integrated with existing BI tools?

You can integrate data lake tools like Snowflake, Amazon S3, Apache Spark, Hadoop, and more with existing BI tools.

4. What are the security considerations when using data lake tools?

The critical security considerations with data lake tools are:

• Data Protection: The tool should have compliance with security features like encryption, lineage tracking, and audit logs.
• Access Control: Data lakes are prone to attacks, as they contain all your business information. Proper access control is necessary to protect sensitive information. 
• Data Governance: When you store your business data, it is essential to manage who has access to it and the purpose of utilizing it.