Data continues to grow in importance for customer insights, predicting trends, and training artificial intelligence (AI) or machine learning (ML) algorithms. For complete coverage of all data sources, the data researcher maximizes the size and scope of available data by bringing all company data together in her one place.
On the other hand, storing all your important data in one place can make it an attractive target for hackers who continuously probe your defenses for vulnerabilities, and the penalties for data breaches are huge. may become. IT security teams need a system that allows them to distinguish and separate different categories of data and protect them from misuse.
Data lakes provide a current solution for maximizing data availability and protection. For large enterprises, data managers and data security teams can choose from a variety of data lake vendors to meet their needs.
However, while everyone can create a data lake, not everyone has the resources to scale, derive value, and protect resources on their own. Fortunately, vendors provide robust tools that allow small teams to reap the benefits of data lakes without having to manage the same resources.
Please refer to Top data lake solutions
What is a data lake?
A data lake creates a single repository for your organization’s raw data. Data feeds ingest data from databases, SaaS platforms, web crawlers, and even edge devices such as security cameras and industrial heat pumps.
Similar to huge hard drives, data lakes also incorporate a folder structure and security can be applied to specific folders to restrict user and application access, read/write and delete permissions. However, unlike hard drives, data lakes should be able to grow in size forever, without having to delete data due to space limitations.
Data lakes support all data types, scale automatically, and support a wide range of analytics, from built-in capabilities to external tools supported by APIs. Analytics tools can perform metadata or content searches, or classify data without changing the underlying data itself.
Self-service data lake tool
Technically, if a company can fit all their data on a single hard drive, it’s a data lake. However, most organizations have astronomically more data than that, and large companies need huge repositories.
Some organizations create their own data lakes in their own data centers. This effort requires more investment in:
- Capital investment: building, hardware, software, access control system
- Operating expenses: power, cooling system, high capacity internet/network connection, maintenance and repair costs
- Labor costs: IT and IT security personnel to maintain hardware, physical security
Vendors in this category provide the tools teams need to create their own data lakes. Organizations that choose these options must contribute more time, money, and expertise to build, integrate, and secure their data lakes.
Apache: Hadoop & Spark
The Apache open source project provides the foundation for many cloud computing tools. To create a data lake, an organization can combine Hadoop and Spark to create the base infrastructure and consider related projects or third-party tools within the ecosystem to build capabilities.
Apache Hadoop provides scalable, distributed processing of large data sets with unstructured or structured data content. Hadoop provides a data storage solution and basic search and analysis tools.
Apache Spark provides a scalable open-source engine to batch data, stream data, perform SQL analytics, train machine learning algorithms, and perform exploratory data analysis (EDA) on massive data sets . Apache Spark provides in-depth analytical tools for more advanced data exploration than is available in basic Hadoop deployments.
Hewlett Packard Enterprise (HPE) Green Lake
HPE GreenLake Services provide pre-integrated hardware and software that can be deployed in your internal data center or colocation facility. HPE handles the heavy lifting of deployment and bills clients based on usage.
HPE monitors usage, scales Hadoop data lake deployments as needed, and provides design and deployment support for other applications. The service accelerates general Hadoop in-house deployments by outsourcing some of the workforce and expertise to his HPE.
cloud data lake tools
Cloud data lake tools provide the infrastructure and basic tools needed to deliver a turnkey data lake. Customers use built-in tools to connect data feeds, storage, security, and APIs to access and explore data.
After choosing an option, some software packages are already integrated with your data lake at launch. When a customer chooses the Cloud Her option, it is ready to ingest data immediately, eliminating the need to wait for shipments, hardware installations, software installations, and more.
However, in order to maximize the customizability of the data lake, these tools tend to push more responsibility onto the customer. Connecting data feeds, analyzing external data, or applying security becomes a more manual process compared to full-service solutions.
Some data lake vendors offer data lakehouse tools to connect to the data lake and provide interfaces for data analysis and transfer. Other add-on tools may also be available that provide functionality available in full-service solutions.
Customers can choose the bare minimum data lake and then do the heavier work or pay extra for the ability to create a more full service version. These vendors also tend not to encourage multi-cloud development and focus on driving more business towards their cloud platforms.
Amazon Web Services (AWS) Data Lake
AWS offers a huge number of options for cloud infrastructure. The company’s data lake service offers an auto-configured collection of core AWS services for storing and processing raw data.
Built-in tools allow users or apps to analyze, manage, search, share, tag, and transform subsets of data with internal or external users. The federation template integrates with Microsoft Active Directory and incorporates existing data segregation rules already deployed within your enterprise.
Google cloud
Google can provide a data lake solution that can store an entire data lake, or simply help process data lake workloads from external sources (usually internal data centers). Google Cloud, when moving from an on-premises Hadoop deployment to one hosted on Google Cloud, Cut costs by 54%.
Google offers its own BigQuery analytics that capture data in real-time using our streaming ingestion feature. Google supports Apache Spark and Hadoop migrations, integrated data science and analytics, and cost management tools.
Microsoft Azure
Microsoft’s Azure Data Lake solution deploys Apache Spark and Apache Hadoop as fully managed cloud services, as well as other analytics clusters such as Hive, Storm, and Kafka. Azure Data Lake includes Microsoft solutions for enterprise-grade security, auditing, and support.
Azure Data Lake easily integrates with other Microsoft products and your existing IT infrastructure and is fully scalable. Customers are able to define and launch data lakes very quickly, and their familiarity with other Microsoft products makes navigating their options intuitive.
Please refer to Top big data storage tools
Full-service data lake tool
Full-service data lake vendors add layers of security, add easy-to-use GUIs, and limit some features in favor of ease of use. These vendors may offer additional analytics capabilities built into their products to provide additional value.
Some companies cannot or strategically choose not to store all their data in one cloud provider. Other data managers may simply need a flexible platform, or may be looking to stitch together data resources from acquired subsidiaries that use different cloud vendors.
Most of the vendors in this category do not offer data hosting and are data manager agnostic, facilitating the use of multi-cloud data lakes. However, some of these vendors offer their own cloud solutions, offering fully integrated full-service offerings that can access multiple clouds and migrate data to a fully controlled platform. I’m here.
Cloudera Cloud Platform
Cloudera’s data platform provides integrated software to ingest and manage data lakes that can span public and private cloud resources. Cloudera not only optimizes your workloads based on analytics and machine learning, but also provides a unified interface to protect and manage your platform data and metadata using a unified interface.
cohesion
Cohesity’s Helios platform provides an integrated platform that delivers a data lake and analytics capabilities. This platform may be licensed as a SaaS solution, as software for self-hosted data lakes, or as software for partner-managed data lakes.
data brick
Databricks offers data lake houses and data lake solutions built on open source technology with integrated security and data governance. Customers can explore data, collaboratively build models, and access preconfigured ML environments. Databricks works across multiple cloud vendors and manages data repositories through a unified interface.
Domo
Domo provides a platform that enables any data lake solution, from storage to application development. Domo augments existing data lakes, or customers can host their data in the Domo cloud.
IBM
IBM’s cloud-based data lake can be deployed on any cloud and incorporates governance, integration, and virtualization into the core principles of the solution. IBM’s data lake can access not only IBM’s pioneering Watson AI for analytics, but also many other IBM tools for querying, scalability, and more.
Oracle
Oracle’s Big Data Service deploys a private version of Cloudera’s cloud platform and integrates with its own Data Lakehouse solution and Oracle cloud platform. Oracle builds on its mastery of database technology to provide powerful tools for data querying, data management, security, governance, and AI development.
snowflake
Snowflake offers a full-service data lake solution that can integrate storage and compute solutions from AWS, Microsoft, or Google. Data managers don’t need to know how to set up, maintain, or support servers and networks, so they can use Snowflake without establishing a cloud database in advance.
Also read: Snowflake vs Databricks: Comparing Big Data Platforms
Choosing a Data Lake Strategy and Architecture
The importance of data analytics continues to grow as businesses make more and more use of different types of data. A data lake provides options for storing, managing, and analyzing all your organization’s data sources. Organizations try to figure out what is important and useful.
This article outlines different strategies and different technologies available for deploying a data lake. The vendor list is not comprehensive and new competitors are constantly entering the market.
Don’t start with vendor selection. Start by understanding the company resources available to support your data lake.
With fewer resources available, businesses should probably pursue full-service options over in-house data centers. However, many other important characteristics, such as: play a role.
- business use case
- AI compatibility
- Searchability
- Compatibility with data lakehouses and other data exploration tools
- safety
- data governance
Established data lakes can be moved, but this can be a very expensive proposition as most data lakes will be huge. Organizations should take the time to run small-scale tests before committing fully to a single vendor or platform.
Read the following: Top 10 data companies
