Best Data Lake Solutions for Hadoop

Scalytics Connect combines data mesh and in-situ data processing with polystore technology, resulting in increased data scalability, increased data processing speed, and multiplying data analytics capabilities without losing privacy or security. You take advantage of all your data without wasting time with data copy or movement, enable innovation with enhanced data analytics, generative AI and federated learning (FL) developments. Scalytics Connect enables any organization to directly apply data analytics, train machine learning (ML) or generative AI (LLM) models on their installed data architecture.

The classic data lake is often reduced to simple but inexpensive raw data storage. This neglects important aspects like data quality, security, and transformation. These topics are left to data scientists who spend up to 80% of their time cleaning, understanding, and acquiring data before they can use their core competencies. Additionally, traditional Data Lakes are often implemented in different departments using different standards and tools. This makes it difficult to implement comprehensive analytical use cases. Smart Data Lakes address these issues by providing methodical and architectural guidelines as well as an efficient tool to create a strong, high-quality data foundation. Smart Data Lakes are the heart of any modern analytics platform. They integrate all the most popular Data Science tools and open-source technologies as well as AI/ML. Their storage is affordable and scalable, and can store both structured and unstructured data.

Datametica's birds have unmatched capabilities, which help to eliminate business risks, time, frustration, anxiety, and cost from the entire process for data warehouse migration to cloud. Datametica's automated product suite allows you to migrate existing data warehouses, data lakes, ETL, Enterprise business intelligence, and other data to the cloud environment of choice. Designing an end to end migration strategy that includes workload discovery, assessment and planning. From the discovery and assessment of your data warehouse to the planning of the migration strategy, Eagle provides clarity on what needs to be migrated, in what order, how to streamline the process, and what the costs and timelines are. The integrated view of the workloads and planning minimizes migration risk without affecting the business.

Data Lake Governance Center (DGC) is a one-stop platform for managing data design, development and integration. It simplifies big data operations and builds intelligent knowledge libraries. A simple visual interface allows you to build an enterprise-class platform for data lake governance. Streamline your data lifecycle, use metrics and analytics, and ensure good corporate governance. Get real-time alerts and help to define and monitor data standards. To create data lakes faster, you can easily set up data models, data integrations, and cleaning rules to facilitate the discovery of reliable data sources. Maximize data's business value. DGC can be used to create end-to-end data operations solutions for smart government, smart taxation and smart campus. Gain new insights into sensitive data across your entire organization. DGC allows companies to define business categories, classifications, terms.

AnalyticsCreator lets you extend and adjust an existing DWH. It is easy to build a solid foundation. The reverse engineering method of AnalyticsCreator allows you to integrate code from an existing DWH app into AC. So, more layers/areas are included in the automation. This will support the change process more extensively. The extension of an manually developed DWH with an ETL/ELT can quickly consume resources and time. Our experience and studies found on the internet have shown that the longer the lifecycle the higher the cost. You can use AnalyticsCreator to design your data model and generate a multitier data warehouse for your Power BI analytical application. The business logic is mapped at one place in AnalyticsCreator.

Open, hybrid data lakes for AI and analytics can be used to put your data to use, wherever it is located. Connect your data in any format and from anywhere. Access it through a shared metadata layer. By matching the right workloads to the right query engines, you can optimize workloads in terms of price and performance. Integrate natural-language semantic searching without the need for SQL to unlock AI insights faster. Manage and prepare trusted datasets to improve the accuracy and relevance of your AI applications. Use all of your data everywhere. Watsonx.data offers the speed and flexibility of a warehouse, along with special features that support AI. This allows you to scale AI and analytics throughout your business. Choose the right engines to suit your workloads. You can manage your cost, performance and capability by choosing from a variety of open engines, including Presto C++ and Spark Milvus.

Talend Data Fabric's cloud services are able to efficiently solve all your integration and integrity problems -- on-premises or in cloud, from any source, at any endpoint. Trusted data delivered at the right time for every user. With an intuitive interface and minimal coding, you can easily and quickly integrate data, files, applications, events, and APIs from any source to any location. Integrate quality into data management to ensure compliance with all regulations. This is possible through a collaborative, pervasive, and cohesive approach towards data governance. High quality, reliable data is essential to make informed decisions. It must be derived from real-time and batch processing, and enhanced with market-leading data enrichment and cleaning tools. Make your data more valuable by making it accessible internally and externally. Building APIs is easy with the extensive self-service capabilities. This will improve customer engagement.

Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.