A data lake is a main storage repository that can hold vast amounts of raw, unstructured data. A data lake is not the same as a data warehouse, which maintains data in structured files. 

Five key takeaways about data lake tools:

  1. Data warehouses cannot process unstructured data, are expensive to maintain, and are inflexible by nature. 

  2. The development of the data lake addressed these warehouse limitations.

  3. Data warehouses require structured data, but data lakes can store and process data regardless of format, including documents, images, videos, and audio files.

  4. Data lakes can store raw (or unstructured), semi-structured, and structured forms of data, which helps support varied use cases in machine learning and analytics. 

  5. Many use cases can make use of data regardless of its state with data lake tools. Companies can consolidate and store all their data in the same repository.

A data warehouse uses a hierarchical structure, whereas the architecture of a data lake is flat. Data warehouses store data within that hierarchy by type, while a data lake’s object storage assigns a metadata tag and unique ID to each piece of data so it can be retrieved from anywhere within the data lake. Since object storage uses an open format, data lake tools allow multiple disparate applications to economically access and use the data housed within.

Today’s organizations are using advanced data analytics apps, artificial intelligence, and machine learning—all of which are challenging to work with if you rely solely on a data warehouse.

Importance of Data Lake Tools

A data lake is an important aspect of data management for numerous reasons—most of all, data lakes unify all data created and generated within a business. Rather than only seeing a snapshot, you get the full picture thanks to centralized, flexible, and thorough data, regardless of its format.

Data lakes are vital for businesses that:

  • Value uncomplicated data management

  • Require a flexible approach to data storage and processing

  • Appreciate real-time analysis of large datasets

  • Experience bottlenecks and data silos in a data warehouse

  • Have data teams and non-technical employees who need to access disparate data in the manner they choose

  • Need to store and manage structured line-of-business datasets as well as unstructured types of data flowing in from new sources

Data lakes help today’s organizations leverage holistic data that deliver actual, up-to-the-minute insights company-wide, rather than just a peek into a department or two. And it’s this ability—structured, semi-structured, or unstructured data ingestion, as well as other benefits mentioned—that makes data lakes the perfect data storage for today’s data teams. 

Table of Contents:

Understanding Data Lake Tools

Data lake tools are the various platforms and providers that offer data lake access or creation. Like many of the tools in today’s tech stacks, you can opt for an on-premise, cloud, or hybrid data lake. To best understand data lake tools, let’s take a look at their components, also known as data lake architecture.

Data lake architecture is flat—think the wide-open “white space” in The Matrix where Morpheus and Neo sit in those overstuffed chairs. 

Data lake architecture is like this—flat and empty until an organization chooses the “rooms”, or zones, needed for company data. However, it’s important to note: The mention of “rooms” or zones doesn’t apply to the data lake itself. 

Data lake zones are rather the platforms or tools that you connect to the data lake. Where the zones are located within the architectural framework depends on the zone type.

While this is by no means an exhaustive list, nor does a data lake have to include all of the following zones, data lakes can connect with these basic zones:

  • Landing: Unstructured data coming from apps, software, and other programs lands here.

  • Ingestion: This zone is where data is stored in its original form.

  • Staging/Processing: Data in the staging and processing zone gets transformed (if necessary) to enrich it for applicable use.

  • Refined data: The zone where data lives after it has left the staging/processing zone.

  • Consumption: Think of dashboards, consumers, and user access. This is the user-facing zone where accessible data (which relies on a user’s access permissions) is available for use, analysis, and other tasks.

  • Exploratory: In this zone, machine learning analysts, data analysts, data scientists, and others can “play” around with data to learn more about it and its use cases.

  • Data governance: The governance zone maintains data quality, oversees data audits, manages metadata, and overall helps ensure your business remains in compliance with various data handling laws and data privacy regulations in the regions where you conduct business.

But how did data lakes and data lake tools evolve?

Data warehousing was first coined by William Inmon, the Father of the Data Warehouse, in 1990. Data lakes, on the other hand, are mere infants compared to some of the software programs, equipment, and tools that businesses use today. But technology, like children, grows up fast.

Here are just a few of the evolutions data lakes and data lake tools have seen in a short time:

  • Then: The term “data lake” is believed to have been coined in 2011 but the concept didn’t start to see widespread adoption until around 2015. Companies primarily built data lakes atop Hadoop.

    • Now: Businesses are moving away from Hadoop in favor of other platforms and type of data storage, such as hybrid or fully cloud-based warehouses in search of greater relational performance, improved flexibility, and less oversight.

  • Then: Data lakes were reserved for certain members of the data team, such as data scientists and data analysts reviewing algorithms.

    • Now: Data lakes need to be more user-friendly to support significantly more use cases, such as reporting and exploratory, and more user personas that may not have the same level of tech-savviness as their data team counterparts.

  • Then: Data lakes were seen as for operational requirements only.

    • Now: Data lakes are embraced for analytical workload capacity, leading to the development of data lake tools and data lakes based in a hybrid or cloud environment.

These data lake evolutions improved user support, allowed development of additional use cases, improved flexibility, and aligned data curation methods with more sophisticated practices.

The Top 5 Data Lake Tools

As technology evolves, the tools we need to access it evolve as well. Today’s organizations use these tools to connect with company data lakes:

  1. Amazon S3

  2. Azure Blob Storage

  3. Google Cloud Storage

  4. Snowflake

  5. Databricks

These data lake tools make the top five for being feature-rich, user-friendly, and affordable.


1. Amazon S3 (Simple Storage Service)

Rating: 4.6/5 (G2)

Key Features:

  • Object-based storage for highly unstructured data

  • All data stored in one flat directory

  • Unified access controls, security, and compliance

  • Flexible pricing plans

Amazon S3, or Simple Storage Service, is an object-based data lake storage solution that allows users to store, use, and manage highly unstructured data all in one bucket in a user-friendly environment.

Use Cases:

  • Data lakes

  • Websites

  • Mobile apps

  • Archives

  • Big Data analysis

  • Internet of Things connected devices

Packages and Pricing: Amazon S3 pricing is complex, to say the least. While Amazon posts a per-GB price for storage, those prices fluctuate depending on actual data consumption, tiering, storage, and geolocation. 

Free trial available: Yes

2. Azure Blob Storage

Rating: 4.6/5 (G2)

Key Features:

  • Microsoft Entra ID for authentication

  • Role-based access controls (RBACs)

  • At-rest encryption

  • Advanced threat security

  • Lifecycle management

  • Optimized for data lakes

Secure, scalable data lake storage tool that offers high performance computing power and machine learning capabilities.

Use Cases:

  • Building effective, native cloud apps

  • Cost-efficient data storage

  • Building impressive data lakes

  • Scaling up or out for IoT connected devices

Packages and Pricing: Like Amazon S3, Azure Blob Storage pricing is pay-as-you-go, highly scalable, and based on factors such as data format and geolocation.

Free trial available: Yes

H3 3. Google Cloud Storage

Rating: 4.6/5 (G2)

Key Features:

  • Seamlessly integrates with other Google Cloud products

  • Can deploy intricate data pipelines

  • Can work with Big Data

  • Scale up or down as needed

  • Security is priority with encryption, auditing, and access controls

  • Affordable data storage option 

Google Cloud Platform is a public cloud vendor offering a suite of cloud-based computing services for data management, storage, and more.

Use Cases:

  • Store and analyze multiple data formats

  • Incorporate machine learning options

  • Integrates with popular business intelligence solutions

Packages and Pricing: Pay-as-you-go-format for 25+ different tools

Free trial available: $300 in credits to try the platform

4. Snowflake

Rating: 4.5/5 (G2)

Key Features:

  • Consistent and reliable data using ACID transactions

  • Data schema enforcement

  • Data lineage tracking

  • Audits and compliance checkups

As a cloud-based, cloud computing platform and data warehouse, Snowflake can scale up or down to meet the needs of growing businesses.

Use cases:

  • Store and analyze data from multiple sources

  • Feature-rich machine learning capabilities

  • Unified data for business intelligence insights

Packages and Pricing: Like many of the other tools in this list, Snowflake pricing is geolocation-based. Download the Snowflake pricing guide to learn more.

Free trial available: Yes; 30-day/$400 value

5. Databricks

Rating: 4.5/5 (G2)

Key Features:

  • Backwards compatible

  • Consistently reliable ACID transactions

  • Fully compatible with all Databricks products, such as:

  • Databricks Lakehouse Platform

  • Databricks SQL Analytics 

From the Databricks website: “Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse.”

Use cases:

  • Store and analyze large datasets

  • Use Delta Lake for analysis in real-time

  • Train your machine learning models

Packages and Pricing:

Databricks offers two types of packages:

  • Pay-as-you-go

  • Committed Use (with discounts)

Various products are available within each package and pricing is based on per-second granularity, geolocation, and business requirements.

Free trial available: Yes

How to Choose the Right Data Lake Tool for Your Team

Deciding on data management solutions for your organization is as unique an experience as the challenges you face. Choosing the data lake tool that’s right for your business depends on a variety of factors, such as how you plan to use it, what you want to gain from it, and even your industry.

That said, some advice applies across the board for all organizations, such as:

Review Your Budget

Data lake tools typically have ongoing costs in addition to your initial purchase. For instance, most data lake tools are subscription-based, and while pricing typically fluctuates with use, it’s important to roughly calculate ahead of time your projected:

  • Data volume

  • Data storage

  • Required features

  • Necessary integrations

Then you’ll need to add any data lake tool setup fees. If the platform charges fees for training your team how to use the tool, verify if the fees are per person, per class, etc.

Confirm Platform Security

Data lake providers should already be well aware of cybersecurity requirements across industries and the most recent threats. When scouting for the right data lake provider, ask about:

  • Data encryption capabilities

  • User access controls

  • Risk monitoring and mitigation

Verify Your Shortlist’s Supported Data Formats

By their very nature, data lakes are built to support a wide variety of data formats. However, depending on multiple factors, different formats can react differently, impacting a data lake’s functionality. 

Make sure the provider you choose supports popular formats, including:

  • CSV

  • Parquet

  • JSON

Data lakes are often optimized for one of these three formats.

Ensure Compatibility with Your Current Data Management Solutions

A data lake is just one part of a larger data management architecture that includes such tools as data analytics solutions, data processing tools, machine learning platforms, and more. While your data team is used to your current setup, different members of your team access and use your data for different reasons, meaning any new programs or platforms must account for each role on your team.

Ensure you don’t have to replace any other programs before you settle on a data lake provider—this will ease your team’s transition and confirm that the solution you choose will integrate well with your existing architecture.

Implementing Data Lake Tools Effectively

Any updates to an organization’s usual way of doing things, while offering opportunities for individual and company growth, aren’t without challenges. 

For a successful data lake implementation, start small. Begin with a pilot project to test capabilities and gain experience before a broader rollout. Place the most emphasis on use cases with the most value.

Document policies and processes from the beginning so all teammates can find, understand, and trust the information in the data lake.

Role-based access is also an important measure. It helps ensure only the individuals who need access to specific data are the ones with access. Access controls, access audits, and data encryption security measures should not be overlooked.

Once you’ve adopted the data lake, there are considerations to concerning integration best practices as well. For instance:

  • Build reusable pipelines and CLIs for moving data from your source tools into the lake and automate whatever you can.

  • Tag, categorize, and catalog ingested data with metadata that facilitates discovery.

  • Use schemas and serialization formats that provide structure while still being flexible.

  • Implement backward compatibility guidelines for schema evolution.

  • Validate and cleanse incoming data to ensure quality.

Some of the most common challenges that arise when adopting a data lake include:

  • Wrestling with unstructured data and a lack of governance can lead to "data swamps".

  • Integrating diverse data sources with incompatible formats, interfaces, and/or semantics.

  • Difficulty supporting both analytical and operational workloads from a single platform.

  • Legacy analytics tools that lack simple connectivity or compatibility with data lake architectures.

But there are success stories, too! For instance, in 2008, Netflix was going through major disruptions due to corruption of its database. The streaming giant took its data management needs to its industry rival, Amazon, and made the jump to cloud-based data management via Amazon Web Services (AWS).

The Future of Data Lake Tools

Technology evolves at near lightspeed, and being in business means being in step with emerging trends and technologies, such as:

  • Streamlined governance. Automation and metadata management using machine learning to categorize data and build pipelines.

  • Massive dataset handling. Cloud-native storage optimized for analytics like AWS Redshift Spectrum and Azure Synapse Analytics.

  • More holistic views. Data fabric platforms that integrate data lakes, warehouses, and other sources seamlessly across locations.

  • Greater self-service. Embed analytics and data science toolkits directly within lakehouses for improved self-service.

  • In-memory processing. Augment lakes with in-memory processing for real-time analytics on streaming data at scale.

  • More granular insights. Data observability, lineage tracking, and impact analysis platforms to manage data pipelines and map interdependencies.

Integrating Data From Multiple Sources into Data Lakes with Integrate.io

Data lake tools can draw data from multiple sources into a data lake, but it’s not always a straightforward process. It can be challenging to navigate different data formats and subpar data quality, and scalability can be an issue sometimes, too. 

That’s why it’s important to have a systematic approach to ETL and use only the best data lake tools.

If your organization is looking to streamline data integration and leverage the power of ETL tools in a data lake, Integrate.io can help. Our cloud-based ETL capabilities, user-friendly visualized pipelines, drag-and-drop interface, and low-code data integration can help you build data-rich pipelines specific to your use case. 

With Integrate.io, you get powerful features, such as:

  • Ingest structured or unstructured data from any source

  • 220+ low-code data transformations

  • Scalability for all data volumes

  • Real-time monitoring & alerts

  • 24/7 industry-leading customer support

Try our ETL data tools and see for yourself how we can help you unlock your data's potential. Sign up for a 14-day free ETL Trial to get a feel of how the platform can help with your data integration needs. Or alternatively, schedule a platform demo with one of our experts to learn how to make the most of your trial.

FAQs

What are the key differences between data lakes and data warehouses?

Data lakes can store unstructured, semi-structured, or structured data in its native format, while data warehouses can only store structured, analyzed data that is optimized for querying and reporting. Data lakes have no schema requirements and can ingest data at scale from many sources. Data warehouses do have schema requirements for data used with BI tools.

How do data lake tools support big data analytics?

Data lake tools provide scalable storage and distributed computing capabilities to handle large volumes and varieties of data. They support running real-time and batch analytics.

Can data lake tools be integrated with existing BI tools?

Many data lake options offer standard integration or connectivity to BI tools like Tableau, Power BI, Looker, and others. For example, they might allow a SQL query from your BI tool directly to the data lake. That query can then pull data into the BI tool to add to its visualizations and reports.

The data lake then feeds the structured data those BI tools need to continue providing business insights.

What are the security considerations when using data lake tools?

Granular, role-based access controls, encryption, data masking, and auditing are crucial for governance. Security capabilities like Ranger, Sentry, and integration with AAA systems are offered by most data lake platforms.

How do data lake tools facilitate real-time data processing?

immediately process and analyze information as it comes into the system, also known as real-time processing. This is extremely valuable for time-sensitive insights or responding immediately to events. 

Data lake tools have special streaming analytics engines and in-memory caching technologies designed for immediate data analysis, which allows constant processing of incoming data without delay or lag time. The results can instantly feed monitoring dashboards, apps via API, or custom rules that act accordingly.

Are there cost-effective data lake tools suitable for small teams?

Yes, cloud-native data lakes on object stores like Amazon S3 offer pay-as-you-go pricing. Open-source tech like Apache Hudi on cloud infrastructure can provide data lake capabilities without heavy licensing costs. These can scale on demand.

How important is user support and community in selecting a data lake tool?

Crucial. An active community highlights experience and maturity in the data management industry, and it means documented solutions to common issues will be available. Professional support may be critical for meeting SLAs in enterprise deployments. Having access to strong customer service and support and a knowledgeable user community can shorten your time-to-value.

What are the best practices for migrating data to a data lake?

Best practices for migrating data to a data lake include planning ahead, incorporating a strong ETL tool or platform, cleaning up data as you go, confirming all analysis tools, dashboards, and apps are working properly, and making sure sensitive information has the appropriate access controls in place and is encrypted.