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DataLakeHouse
-
1-10 Employees
2019
Key takeaway
DataLakeHouse.io (DLH.io) specializes in data orchestration, ELT, and data security, providing a streamlined platform that allows businesses to automate workflows and focus on delivering insights. With its pre-built source connector integrations and a strong emphasis on collaboration and efficiency, DLH.io transforms data management, making it easier to harness data for strategic decision-making.
Reference
Core business
DataLakeHouse.io
Lakebed.io
Vancouver, Canada
A
1-10 Employees
2018
Key takeaway
Lakebed serves as a central hub for storing and managing data from various sources, allowing users to easily create reports and gain a comprehensive view of their marketing initiatives. With its user-friendly interface and ability to quickly install and populate a data lake, Lakebed simplifies the process of transforming disorganized data into actionable intelligence.
Reference
Core business
Lakebed.io | Data lakes made easy
Lake Network
Bengaluru, India
D
1-10 Employees
2019
Key takeaway
Lake offers secure, reliable, and decentralized file storage, making it an ideal partner for apps that require efficient data management. With SDKs available for easy integration into Desktop, Web, and Mobile applications, Lake supports developers in creating robust decentralized storage solutions for DApps and blockchains.
Reference
Core business
Lake - Decentralized Storage for DApps and Blockchains.
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SmartDataLake
Vienna, Austria
A
11-50 Employees
-
Key takeaway
SmartDataLake focuses on creating innovative methods and tools for large-scale analytics specifically for Big Data Lakes.
Reference
Core business
SpazioDati | SmartDataLake
Databricks Ventures
San Francisco, United States
B
1001-5000 Employees
2013
Key takeaway
Databricks is highlighted as a leader in the data lake space, offering a unique lakehouse platform that integrates the advantages of both data lakes and data warehouses. This approach allows users to unlock the full potential of their data, particularly through Azure Databricks.
Reference
Product
Data Lakehouse Platform – Databricks
Click to learn how to unlock the potential of your data lake with Azure Databricks.
BSgroup Data Analytics AG
Zurich, Switzerland
A
11-50 Employees
2016
Key takeaway
The company specializes in Data Analytics and Data Science, highlighting the benefits of utilizing an Azure Data Lake from Microsoft to manage and leverage large volumes of data effectively. Their expertise is aimed at helping businesses make informed decisions by addressing Big Data challenges through modern data platforms.
Reference
Product
Data Lake - BSGROUP DATA ANALYTICS AG
Data Lake - make smart decisions by using a wide variety of data. Master the Big Data challenges with our solutions for Data Lake.
DataTheta
Chennai, India
D
11-50 Employees
2017
Key takeaway
DataTheta emphasizes the importance of data discovery and engineering in analytics initiatives, making it well-equipped to support businesses in leveraging their data effectively. Their expertise in providing customizable dashboards and reports highlights their commitment to enhancing data visualization and informed decision-making.
Reference
Product
Data Lake - DataTheta
Treeverse
Tel-Aviv, Israel
B
11-50 Employees
2020
Key takeaway
lakeFS by Treeverse is a data version control platform that simplifies the lives of data engineers, data scientists, and analysts, enabling them to manage their data like code. With features that support best practices in data engineering, lakeFS enhances data quality and allows for effective testing and experimentation.
Reference
Core business
Git for Data - lakeFS
lakeFS is an open-source data version control that transforms your object storage to Git-like repositories. Start managing data the way you manage your code.
Loamics
Boulogne-Billancourt, France
A
51-100 Employees
-
Key takeaway
Loamics specializes in data management solutions, particularly through its Data Lake architecture, which facilitates data-driven decision-making by cross-referencing diverse and secure data in real time. This approach accelerates digital transformation and enhances the use of Data Science and Artificial Intelligence across various business sectors.
Reference
Product
DataLake - Loamics
LOAMICS provides data management solutions on Data Lake architecture forquality machine learning data. It works seamlessly on Microsoft Azure.
Product Data Link
Frederiksberg, Denmark
A
- Employees
2017
Key takeaway
The company emphasizes the importance of its Product Data Lake solution for efficient product information sharing within business ecosystems. By becoming a Product Data Lake ambassador, professionals can help clients connect with trading partners, facilitating the exchange of product data across various standards and methods.
Reference
Core business
About Product Data Lake – Product Data Link
Product Data Lake is a cloud service for sharing product data in the business ecosystems of manufacturers, distributors, merchants, marketplaces and large end users of product information.Product D…
Technologies which have been searched by others and may be interesting for you:
A data lake is a centralized repository that allows organizations to store vast amounts of structured and unstructured data at scale. This storage solution enables users to retain data in its raw form, making it accessible for various analytics and processing tasks. Unlike traditional databases, which require data to be pre-processed and structured, data lakes can accommodate diverse data types, including text, images, and videos, facilitating advanced analytics, machine learning, and real-time processing. In addition, data lakes support big data technologies, allowing organizations to leverage tools like Apache Hadoop and Apache Spark for data processing and analytics. This flexibility helps businesses harness valuable insights from their data, driving better decision-making and innovation.
A data lake stores vast amounts of raw data in its native format until it is needed for analysis. This flexibility allows organizations to accommodate various data types, including unstructured and semi-structured data, which can be ingested without a predefined schema. In contrast, a data warehouse is designed for structured data and requires a defined schema before data can be loaded, optimizing it for complex queries and reporting purposes. The scalability of a data lake enables it to handle large volumes of data, making it suitable for big data analytics. On the other hand, data warehouses offer faster query performance due to their structured nature, but they may struggle with accommodating the diverse data types found in modern data environments.
1. Scalability
Data Lakes offer scalable storage solutions that can accommodate vast amounts of structured and unstructured data. This flexibility allows organizations to grow their data storage needs without significant investment in new infrastructure.
2. Cost-Effectiveness
Utilizing a Data Lake can be more cost-effective compared to traditional data warehousing. By leveraging inexpensive storage options, companies can store large volumes of data without incurring high costs, making it accessible for analysis and processing.
3. Diverse Data Types
Data Lakes support a variety of data formats, including raw data, semi-structured, and structured data. This capability enables businesses to gather insights from diverse sources, enhancing data analysis and decision-making processes.
4. Real-Time Analytics
Data Lakes facilitate real-time data processing, allowing businesses to analyze and act on data as it arrives. This immediacy can lead to quicker insights and more informed business decisions.
5. Enhanced Flexibility
The architecture of Data Lakes provides a high level of flexibility for data ingestion and processing. Organizations can easily adapt to changing data requirements and integrate various data sources without extensive reconfiguration.
1. Big Data Analytics
Data lakes are often employed for storing vast amounts of unstructured data, enabling organizations to perform advanced analytics and gain insights from diverse data sources.
2. Data Archiving
Organizations use data lakes to archive historical data that may not be needed for immediate analytics but is valuable for compliance and future analysis.
3. Machine Learning
Data lakes provide a platform for data scientists to access and process large datasets, facilitating the development and training of machine learning models.
4. Real-Time Data Processing
With the ability to ingest data streams, data lakes support real-time analytics, allowing businesses to respond swiftly to changing conditions.
5. Data Integration
Data lakes serve as a centralized repository, integrating data from various sources, which simplifies data management and enables a holistic view of an organization’s information.
Ensuring security in a Data Lake involves implementing multiple layers of protection. One approach is to utilize encryption for data at rest and in transit, which safeguards sensitive information from unauthorized access. Another essential aspect is access control, where organizations can define user roles and permissions to limit access to specific datasets. Utilizing identity and access management (IAM) tools helps in monitoring and managing user activities, ensuring only authorized personnel can interact with the data. Additionally, regular audits and compliance checks can help identify vulnerabilities and ensure that security policies are being adhered to.
Some interesting numbers and facts about your company results for Data Lake
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 5208 |
| Amount of suitable service providers | 5314 |
| Average amount of employees | 11-50 |
| Oldest suiting company | 2013 |
| Youngest suiting company | 2020 |
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Some interesting questions that has been asked about the results you have just received for Data Lake
What are related technologies to Data Lake?
Based on our calculations related technologies to Data Lake are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Who are Start-Ups in the field of Data Lake?
Start-Ups who are working in Data Lake are Treeverse
Which industries are mostly working on Data Lake?
The most represented industries which are working in Data Lake are IT, Software and Services, Other, Consulting, Logistics, Supply Chain and Transportation, Finance and Insurance
How does ensun find these Data Lake Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
