Best Data Management Software for AWS App Mesh

Cloud-based solution for observability that helps businesses manage and track workload and performance through a single dashboard. Monitor all the services you run on your cloud without compromising cost, granularity or scale. Groundcover is a cloud-native APM solution that makes observability easy so you can focus on creating world-class products. Groundcover's proprietary sensor unlocks unprecedented granularity for all your applications. This eliminates the need for costly changes in code and development cycles, ensuring monitoring continuity.

Amazon DynamoDB, a key-value and document databank, delivers single-digit millisecond performance on any scale. It is a fully managed, multiregional, multimaster, durable database that offers built-in security, backup, restore, and in-memory cache for internet-scale apps. DynamoDB can process more than 10 trillion requests per hour and can handle peak requests of more than 20,000,000 requests per second. Many of the fastest-growing businesses in the world, such as Lyft, Redfin, and Airbnb, as well as enterprises like Samsung, Toyota and Capital One, rely on DynamoDB's scale and performance to support mission-critical workloads.

Microsoft SQL Server 2019 includes intelligence and security. You get more without paying extra, as well as best-in-class performance for your on-premises requirements. You can easily migrate to the cloud without having to change any code. Azure makes it easier to gain insights and make better predictions. You can use the technology you choose, including open-source, and Microsoft's innovations to help you develop. Integrate data into your apps easily and access a rich set cognitive services to build human-like intelligence on any data scale. AI is built into the data platform, so you can get insights faster from all of your data, both on-premises or in the cloud. To build an intelligence-driven company, combine your enterprise data with the world's data. You can build your apps anywhere with a flexible platform that offers a consistent experience across platforms.

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 Relational Database Service (Amazon RDS), makes it easy to create, manage, and scale a cloud-based relational database. It offers a cost-efficient, resizable storage capacity and automates time-consuming admin tasks like database setup, patching, backups, and hardware provisioning. It allows you to concentrate on your applications, so they can provide the high performance, security, compatibility, and high availability that they require. Amazon RDS can be used on several database instance types, optimized for memory, performance, or I/O. It offers six familiar database engines to choose, including PostgreSQL and MySQL, MariaDB, Oracle Database and SQL Server. To easily replicate or migrate your existing databases to Amazon RDS, you can use the AWS Database Migration Service.

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora is five times faster that standard MySQL databases and three time faster than standard PostgreSQL database. It offers the same security, availability, reliability, and cost-effectiveness as commercial databases, but at a fraction of the cost. Amazon Aurora is fully managed and maintained by Amazon Relational Database Service, (RDS). This automates tedious administration tasks such as hardware provisioning, database setup, patching and backups. Amazon Aurora is a distributed, fault-tolerant and self-healing storage that auto-scales up 64TB per database instance. It offers high availability and performance with up to 15 low latency read replicas, point in time recovery, continuous backup to Amazon S3, replication across threeAvailability Zones, and continuous backup to Amazon S3.

Amazon WorkDocs allows you to securely create, store, and collaborate with content. Amazon WorkDocs makes it easy to create, edit, and share content. It's centrally stored on AWS so you can access it from any device. Amazon WorkDocs allows you to collaborate with others and allows you to easily share content, give rich feedback, and collaboratively edit documents. Amazon WorkDocs can be used to move file shares to the cloud and retire legacy file sharing infrastructure. Amazon WorkDocs allows you to integrate with existing systems and provides a rich API for you to develop your own content-rich apps. Amazon WorkDocs is built using AWS, where your content will be protected on the largest cloud infrastructure. Amazon WorkDocs has no upfront fees and no commitments. Only active user accounts and the storage you use are charged.

AWS Database Migration Service allows you to migrate databases to AWS quickly, securely, and securely. The migration process does not affect the source database's functionality. This minimizes downtime for applications that depend on the database. The AWS Database Migration Service is able to migrate your data from and to most commonly used commercial and open-source database systems.

Amazon Redshift is preferred by more customers than any other cloud data storage. Redshift powers analytic workloads for Fortune 500 companies and startups, as well as everything in between. Redshift has helped Lyft grow from a startup to multi-billion-dollar enterprises. It's easier than any other data warehouse to gain new insights from all of your data. Redshift allows you to query petabytes (or more) of structured and semi-structured information across your operational database, data warehouse, and data lake using standard SQL. Redshift allows you to save your queries to your S3 database using open formats such as Apache Parquet. This allows you to further analyze other analytics services like Amazon EMR and Amazon Athena. Redshift is the fastest cloud data warehouse in the world and it gets faster each year. The new RA3 instances can be used for performance-intensive workloads to achieve up to 3x the performance compared to any cloud data warehouse.

Amazon QuickSight allows everyone within your organization to access your data. This includes asking questions in natural language, exploring interactive dashboards, and automatically looking for patterns or outliers using machine learning. QuickSight powers millions upon millions of dashboard views per week for customers like the NFL, Expedia and Volvo. This allows their end-users make better data-driven decision making. To receive relevant visualizations, ask questions about your data using Q's ML-powered engine. This allows you to ask conversational questions without the need for data preparation by admins and authors. AWS' machine learning expertise allows you to uncover hidden insights in your data, forecast accurately and do what-if analysis. You can also add natural language narratives or easy-to-understand natural languages to your dashboards using AWS' machine learning expertise. You can embed interactive visualizations and dashboards in your applications, as well as sophisticated dashboard authoring and natural language query capabilities.

Amazon Textract, a fully managed machine-learning service, automatically extracts text from scanned documents. It goes beyond optical character recognition (OCR), to identify, understand and extract data from forms or tables. Today, many companies extract data from scanned documents such as PDF's and tables using manual data entry. This can be slow, expensive, and prone to errors. Or, they use OCR software which requires manual configuration and must be updated every time the form is modified to be usable. Textract uses machine-learning to automatically read and process any type document. It extracts text, forms, tables, and other data without any manual effort or custom code. Textract allows you to quickly automate manual document activities and process millions of pages in just hours.

AWS Data Pipeline, a web service, allows you to reliably process and transfer data between different AWS compute- and storage services as well as on premises data sources at specific intervals. AWS Data Pipeline allows you to access your data wherever it is stored, transform it and process it at scale, then transfer it to AWS services like Amazon S3, Amazon RDS and Amazon DynamoDB. AWS Data Pipeline makes it easy to create complex data processing workloads that can be fault-tolerant, repeatable, high-availability, and reliable. You don't need to worry about resource availability, managing intertask dependencies, retrying transient errors or timeouts in individual task, or creating a fail notification system. AWS Data Pipeline allows you to move and process data previously stored in on-premises silos.

Amazon Neptune is a fully managed graph database service that allows you to quickly and reliably build applications that can work with highly connected data sets. Amazon Neptune's core is a purpose-built graph database engine that can store billions of relationships and query the graph with only milliseconds latency. Amazon Neptune supports the popular graph models Property Graph, W3C's RDF, as well as their respective query languages Apache TinkerPop Gremlin, SPARQL. This allows you to quickly build queries that efficiently navigate large datasets. Neptune supports graph use cases like recommendation engines, fraud detection and knowledge graphs. It also powers network security and drug discovery.

AWS Glue, a fully managed extract-transform-and-load (ETL) service, makes it easy for customers prepare and load their data for analysis. With just a few clicks, you can create and run ETL jobs. AWS Glue simply points to the AWS Data Catalog and AWS Glue finds your data and stores metadata (e.g. AWS Glue Data Catalog contains the table definition and schema. Once your data has been cataloged, it is immediately searchable and queryable. It is also available for ETL.

Amazon Forecast is a fully managed service that uses machine-learning to provide highly accurate forecasts. Today, companies use everything from simple spreadsheets and financial planning software to accurately forecast future business outcomes, such as product demand, resource requirements, and financial performance. These tools create forecasts by looking at historical data, also known as time series data. These tools might attempt to predict the future sales for a raincoat using only its past sales data. The assumption is that the past determines the future. This approach is not able to accurately forecast large amounts of data with irregular trends. It is difficult to combine data that changes over time (such price, discounts, and number of workers) with relevant independent variables such as product features and store location.

Amazon ElastiCache makes it easy to create, manage, and scale popular open source compatible in-memory cloud data stores. You can build data-intensive apps and improve the performance of existing databases by retrieving data in high-throughput and low latency, in-memory storages. Amazon ElastiCache is popular for real-time applications such as Caching, Session Stores and Gaming, Geospatial Service, Real-Time Analytics and Queuing. Amazon ElastiCache provides fully managed Redis, Memcached and other services for demanding applications that need sub-millisecond response time. Amazon ElastiCache is an in-memory cache and data store that can support the most demanding applications that require sub-millisecond response time. Amazon ElastiCache delivers secure, lightning fast performance by using an optimized stack that runs on customer-dedicated nodes.

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.

Amazon EMR is the market-leading cloud big data platform. It processes large amounts of data with open source tools like Apache Spark, Apache Hive and Apache HBase. EMR allows you to run petabyte-scale analysis at a fraction of the cost of traditional on premises solutions. It is also 3x faster than standard Apache Spark. You can spin up and down clusters for short-running jobs and only pay per second for the instances. You can also create highly available clusters that scale automatically to meet the demand for long-running workloads. You can also run EMR clusters from AWS Outposts if you have on-premises open source tools like Apache Spark or Apache Hive.

AWS Lake Formation makes it simple to create a secure data lake in a matter of days. A data lake is a centrally managed, secured, and curated repository that stores all of your data. It can be both in its original form or prepared for analysis. Data lakes allow you to break down data silos, combine different types of analytics, and gain insights that will guide your business decisions. It is a time-consuming, manual, complex, and tedious task to set up and manage data lakes. This includes loading data from different sources, monitoring data flows, setting partitions, turning encryption on and managing keys, defining and monitoring transformation jobs, reorganizing data in a columnar format, deduplicating redundant information, and matching linked records. Once data has been loaded into a data lake, you will need to give fine-grained access and audit access over time to a wide variety of analytics and machine learning tools and services.

Amazon Comprehend is a HIPAA-eligible, natural language processing (NLP), service that uses machine-learning to extract health data form medical text. No machine learning experience is necessary. Today, a lot of health data is found in free-form medical texts like doctor's notes, clinical trials reports, and patient records. Manually extracting data can be time-consuming and automated rule-based attempts at extracting data won't capture the whole story because they don't take context into consideration. The data is not usable for large-scale analytics that will help improve the healthcare and life sciences industry, patient outcomes, and increase efficiencies.

Amazon DocumentDB (with MongoDB compatibility), is a fast, scalable and highly available document database service that supports MongoDB workloads. Amazon DocumentDB is a document database that makes it easy to store, query and index JSON data. Amazon DocumentDB is a nonrelational database service that was designed from the ground up to provide the performance, scalability and availability you require when running mission-critical MongoDB workloads on a large scale. Amazon DocumentDB is decoupled from compute and storage, which allows each to scale independently. You can increase read capacity to millions of requests every second by adding up 15 low latency replicas in minutes, regardless how large your data is. Amazon DocumentDB is designed to guarantee 99.99% availability. It replicates six copies across three AWSAvailability Zones (AZs) of your data.