lakeFS Integrations

Looker reinvents the way business intelligence (BI) works by delivering an entirely new kind of data discovery solution that modernizes BI in three important ways. A simplified web-based stack leverages our 100% in-database architecture, so customers can operate on big data and find the last mile of value in the new era of fast analytic databases. An agile development environment enables today’s data rockstars to model the data and create end-user experiences that make sense for each specific business, transforming data on the way out, rather than on the way in. At the same time, a self-service data-discovery experience works the way the web works, empowering business users to drill into and explore very large datasets without ever leaving the browser. As a result, Looker customers enjoy the power of traditional BI at the speed of the web.

Amazon Simple Storage Service (Amazon S3), an object storage service, offers industry-leading scalability and data availability, security, performance, and scalability. Customers of all sizes and industries can use Amazon S3 to store and protect any amount data for a variety of purposes, including data lakes, websites and mobile applications, backup, restore, archive, enterprise apps, big data analytics, and IoT devices. Amazon S3 offers easy-to-use management tools that allow you to organize your data and set up access controls that are tailored to your business, organizational, or compliance needs. Amazon S3 is built for 99.999999999% (11 9,'s) of durability and stores data for millions applications for companies around the globe. You can scale your storage resources to meet changing demands without having to invest upfront or go through resource procurement cycles. Amazon S3 is designed to last 99.999999999% (11 9,'s) of data endurance.

All sizes of companies can use object storage. You can store any amount of data. You can retrieve it as often and as often as you like. You can configure your data with Object Lifecycle Management to automatically transition to lower cost storage classes when it meets certain criteria, such as when it reaches a certain date or when you have stored a newer version. Cloud Storage offers a growing number of storage locations that you can store your data, with multiple redundancy options. You can choose where and how to store your data, regardless of whether you want to optimize for a split-second response or create a robust disaster recovery strategy. Two highly efficient online routes to Cloud Storage are Storage Transfer Service and Transfer Service. Both offer the speed and scalability you need to make data transfers faster. Our Transfer Appliance is a shippable storage device that can be used for offline data transfer.

Open-source web application, the Jupyter Notebook, allows you to create and share documents with live code, equations, and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

Apache Kafka®, is an open-source distributed streaming platform.

AWS offers a wide range of services, including database storage, compute power, content delivery, and other functionality. This allows you to build complex applications with greater flexibility, scalability, and reliability. Amazon Web Services (AWS), the world's largest and most widely used cloud platform, offers over 175 fully featured services from more than 150 data centers worldwide. AWS is used by millions of customers, including the fastest-growing startups, large enterprises, and top government agencies, to reduce costs, be more agile, and innovate faster. AWS offers more services and features than any other cloud provider, including infrastructure technologies such as storage and databases, and emerging technologies such as machine learning, artificial intelligence, data lakes, analytics, and the Internet of Things. It is now easier, cheaper, and faster to move your existing apps to the cloud.

Amazon Athena allows you to easily analyze data in Amazon S3 with standard SQL. Athena is serverless so there is no infrastructure to maintain and you only pay for the queries you run. Athena is simple to use. Simply point to your data in Amazon S3 and define the schema. Then, you can query standard SQL. Most results are delivered in a matter of seconds. Athena makes it easy to prepare your data for analysis without the need for complicated ETL jobs. Anyone with SQL skills can quickly analyze large-scale data sets. Athena integrates with AWS Glue Data Catalog out-of-the box. This allows you to create a unified metadata repositorie across multiple services, crawl data sources and discover schemas. You can also populate your Catalog by adding new and modified partition and table definitions. Schema versioning is possible.

Amazon Simple Email Service (SES), a cost-effective, flexible and scalable email service, allows developers to send mail from any application. Amazon SES can be configured quickly to support multiple email use cases including marketing, transactional, and mass mail communications. Amazon SES's flexibility in IP deployment and email authentication options allow for greater deliverability and protect sender reputation. Additionally, sending analytics measure the impact of each individual email. Amazon SES allows you to send secure, global, and scaled email. You can easily configure email sending using either the Amazon SES console or APIs. Amazon SES also supports email receipt, which allows you to interact with customers at scale. Amazon SES is a subscription service that allows you to pay only for what you use, regardless of the use case or volume of your messages.

Secure, highly scalable object storage that is both highly scalable and scalable for cloud-native workloads. Azure Blob Storage allows you to create data lakes for your analytics and storage to build powerful cloud and mobile apps. Tiered storage reduces costs and allows you to scale up for machine learning and high-performance computing workloads. Blob storage was designed from the ground up for developers of mobile, web and cloud-native applications. It supports the scale, security and availability requirements. It can be used as a foundation for serverless architectures like Azure Functions. Blob storage supports all the most popular development frameworks such as Java,.NET and Python. It is also the only cloud storage service that offers a premium SSD-based object storage tier to support interactive and low-latency scenarios.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

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.

Data management doesn't have to be difficult. SimpleKPI provides everything you need for monitoring and visualizing your business metrics. SimpleKPI is packed with simple-to-use features that make it easy to understand your business performance. This dashboard is simple and easy to use. It takes complex data and converts it into visuals that are easily understood. To share with your colleagues, create high-level summaries about your KPIs. You can choose from a variety of charts, graphs and league tables to help you communicate a clear understanding of your data. It is important to make informed business decisions. We have integrated powerful reporting into every aspect and feature of SimpleKPI. You can get both summary and detailed information to give you a complete picture about your progress towards achieving your goals and targets.

MinIO's high performance object storage suite is software-defined and allows customers to create cloud-native data infrastructures for machine learning, analytics, and application data workloads. MinIO object storage is fundamentally unique. It is 100% open-source and designed for performance and the S3 API. MinIO is ideal to host large, private cloud environments that have strict security requirements. It also delivers mission-critical availability across a wide range of workloads. MinIO is the fastest object storage server in the world. With READ/WRITE speeds up to 183 GB/s on standard hardware and 171GB/s on SSDs, object storage can be used as the primary storage tier for a variety of workloads, including Spark, Presto TensorFlow, Spark, TensorFlow, H2O.ai, and as a replacement for Hadoop HDFS. MinIO uses the hard-earned knowledge of web scalers to bring object storage a simple scaling model. MinIO scales with one cluster that can be federated to other MinIO clusters.

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.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

You can quickly collect, process, analyze, and analyze video and data streams. Amazon Kinesis makes it easy for you to quickly and easily collect, process, analyze, and interpret streaming data. Amazon Kinesis provides key capabilities to process streaming data at any scale cost-effectively, as well as the flexibility to select the tools that best fit your application's requirements. Amazon Kinesis allows you to ingest real-time data, including video, audio, website clickstreams, application logs, and IoT data for machine learning, analytics, or other purposes. Amazon Kinesis allows you to instantly process and analyze data, rather than waiting for all the data to be collected before processing can begin. Amazon Kinesis allows you to ingest buffer and process streaming data instantly, so you can get insights in seconds or minutes, instead of waiting for hours or days.

#1 Contactless Dining Solution. $0 Monthly Fee. We are the largest provider worldwide of contactless dining technology, with more than 100 million active users per month and 300,000+ shipped devices. Find out more about our top-selling product. Our Contactless Dining Solution allows your restaurant to offer a complete contactless dining experience to all of its guests. Guests can view the entire menu, place orders, pay at their table, and much, much more without the need to make contact with anyone. Register today to be completely contactless within 3 days. There are no recurring fees, but regular payment processing charges apply. You don't even need to change your existing POS software. Although the solution is available worldwide, supplies are limited and demand high so make sure to reserve your spot today. Presto is the largest provider of contactless technology in America and Europe, with over 100 million users using it each month and 300,000+ systems being shipped.

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.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Apache Flink is a distributed processing engine and framework for stateful computations using unbounded and bounded data streams. Flink can be used in all cluster environments and perform computations at any scale and in-memory speed. A stream of events can be used to produce any type of data. All data, including credit card transactions, machine logs, sensor measurements, and user interactions on a website, mobile app, are generated as streams. Apache Flink excels in processing both unbounded and bound data sets. Flink's runtime can run any type of application on unbounded stream streams thanks to its precise control of state and time. Bounded streams are internal processed by algorithms and data structure that are specifically designed to process fixed-sized data sets. This results in excellent performance. Flink can be used with all of the resource managers previously mentioned.

Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.