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Ampool Usage Patterns (Part 3): High-Speed Data Mart for Hadoop Data Lakes

From a nascent Apache project in 2006 to being commercially supported data platform by two public companies, Apache Hadoop has come a long way. Initially adopted by Web 2.0 companies, such as Yahoo, Facebook, LinkedIn & Twitter, Hadoop-based data platforms started becoming a major part in enterprise data infrastructure starting 2011. Today, a majority of […]

Ampool Usage Patterns (Part 2): Application Acceleration

Almost all applications are powered by one or more persistent data stores. Relational OLTP DBMSs have been most often used as data serving backends. However, in recent years, for large scale-out applications, non-relational (NoSQL) data stores, that sacrifice consistency for availability, are being used for data serving. Most modern mobile or web-based applications’ user experience […]

Ampool Usage Patterns (Part 1): Near-App Analytics

Wishing our blog readers a very happy new year 2018. In the first blog series of this new year, we will outline three broad patterns where Ampool Active Data Store (ADS) and In-Memory Platform is being used for real use-cases by our customers and pilots. In this post, we will describe the first usage pattern […]

Ampool 2.0 Released

We are excited to announce the Ampool 2.0 release which is an important milestone for Ampool. This release includes many new features and important performance  improvements. Following major features are added: FTable Storage Format The FTable employs block strategy for its in-memory layer that groups multiple records together and minimizes per record storage overhead and […]

Ampool Active Data Store now on AWS Marketplace

Public cloud, once primarily used by agile startups, has taken the enterprise IT infrastructure by storm. Instant fulfillment, a wealth of services, pay-as-you-go model (in addition to reserved resources), multiple deployment options (bare metal, virtual machines, containers, and functions) have attracted application developers in startups and large companies alike to the public clouds. At Ampool, […]

Ampool Active Data Store is Now Open Source

From relatively obscurity two decades ago, Open Source Software has come a long way, and has become a dominating force in enterprises. Most modern data platforms, both operational and analytical, are built with OSS projects, such as Hadoop, Cassandra, MongoDB, Spark, and Kafka. In our experience, many traditional enterprises in financial services, telecom, manufacturing, and […]

Analyzing Data in Ampool ADS using Apache Spark

Apache Spark is a distributed computing framework with implicit parallelism and fault-tolerance. The Ampool ADS is distributed in-memory store with built-in fault-tolerance. Typically Apache Spark caches data into its executor memory for faster processing and then uses the underlying disk based stores as and when needed. The data fetched is immutable and typically it is […]

MTable REST API

Continuing from our previous post on mutable tables, Introducing MTable, we now see how we can interact with this data abstraction through REST API’s. The Ampool developer REST interface runs as an embedded HTTP or HTTPS service (Jetty server) within an Ampool ADS server. For the purpose of documentation, the developer REST API’s are also integrated with […]

Deep Dive with MTable

Continuing from the previous post , In this post we will demonstrate some of the APIs to create and access data in MTable. The example below uses Java APIs to create and populate the data and Spark DataFrame/SQL API to query data from the MTable. Note: Spark SQL also support Hive Query syntax and UDFs. Use case […]

Introducing FTable

The previous post discussed about the mutable data in Ampool Active Data Store (ADS). Here we will discuss about how Ampool ADS enables you to deal with very large volume of immutable data. The FTable stands for “flow-table” and it enables fast ingestion of very large amount of immutable data (aka facts data). The data is internally […]