Best Data Lake Solutions for Apache Spark

Archon Data Store™ is an open-source archive lakehouse platform that allows you to store, manage and gain insights from large volumes of data. Its minimal footprint and compliance features enable large-scale processing and analysis of structured and unstructured data within your organization. Archon Data Store combines data warehouses, data lakes and other features into a single platform. This unified approach eliminates silos of data, streamlining workflows in data engineering, analytics and data science. Archon Data Store ensures data integrity through metadata centralization, optimized storage, and distributed computing. Its common approach to managing data, securing it, and governing it helps you innovate faster and operate more efficiently. Archon Data Store is a single platform that archives and analyzes all of your organization's data, while providing operational efficiencies.

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.

BigLake is a storage platform that unifies data warehouses, lakes and allows BigQuery and open-source frameworks such as Spark to access data with fine-grained control. BigLake offers accelerated query performance across multicloud storage and open formats like Apache Iceberg. You can store one copy of your data across all data warehouses and lakes. Multi-cloud governance and fine-grained access control for distributed data. Integration with open-source analytics tools, and open data formats is seamless. You can unlock analytics on distributed data no matter where it is stored. While choosing the best open-source or cloud-native analytics tools over a single copy, you can also access analytics on distributed data. Fine-grained access control for open source engines such as Apache Spark, Presto and Trino and open formats like Parquet. BigQuery supports performant queries on data lakes. Integrates with Dataplex for management at scale, including logical organization.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

The only fully-managed cloud data lakehouse that can ingest data from all of your sources in minutes, and support all of your query engines on a large scale. All for a fraction the cost. With the ease of fully managed pipelines, you can ingest data from databases and event streams in near-real-time. You can query your data using any engine and support all of your use cases, including BI, AI/ML, real-time analytics and AI/ML. Simple usage-based pricing allows you to cut your costs by up to 50% compared with cloud data warehouses and ETL software. With a fully-managed, highly optimized cloud service, you can deploy in minutes and without any engineering overhead. Unify all your data into a single source and eliminate the need for data to be copied between data lakes and warehouses. Apache Hudi, Apache Iceberg and Delta Lake all offer omnidirectional interoperability, allowing you to choose the best table format for your needs. Configure managed pipelines quickly for database CDC and stream ingestion.

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.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

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.