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Invoking the AWS CLI with Terraform

faun.pub – As awesome and powerful Terraform is, there are times when you find yourself unable to execute certain actions for your automation. This could be due to many reasons including: no Terraform resource…
Quelle: news.kubernauts.io

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AppLovin builds on Google Cloud to transform mobile marketing

Today’s fastest growing companies are building and scaling in the cloud. Turning to a modern cloud as a platform for growth not only enables easy access to capabilities in areas like artificial intelligence (AI) and machine learning (ML), but also a platform on which they can quickly scale an entire business and deliver products to fast-growing customer bases.The mobile app industry is a great example of this—the numbers in mobile apps are many and continue to grow: thousands of developers making millions of apps, downloaded billions of times around the world, to billions of users spending an increasing amount of time online through apps.AppLovin is a leading marketing software company that sits squarely in the middle of this fast-moving, engaging, and yes, challenging market. AppLovin’s technology platform enables developers to market, monetize, analyze and publish their apps. AppLovin’s ML engine makes three trillion daily predictions based on over 6.5 trillion events per day. In the past year alone, AppLovin has driven 7.5 billion downloads on its platform, helping app developers find over 3 billion new users.AppLovin and Google have a strategic partnership, prioritizing the enablement of AppLovin’s marketing, monetization, and app launch/distribution efforts. To bring it all together, and to power AppLovin’s massive growth, AppLovin needed a cloud platform on which it could easily scale its business, and they turned to Google Cloud.Initially, AppLovin delivered its services to customers using co-location, bare metal and cloud. As its growth accelerated and customers’ needs grew, they evaluated consolidating on the cloud as a platform on which to run and scale their business. Today, AppLovin is delivering its core services and applications to customers on Google Cloud’s low-latency, scalable infrastructure, ensuring that AppLovin customers can run campaigns and have access to the tools they need to grow their apps and that AppLovin has a cloud partner who will easily scale alongside them.Beginning their migration to the cloud in early 2021, AppLovin successfully migrated seven data centers to Google Cloud (including five in a single day) with no service disruption to its customers. AppLovin is also using other Google Cloud technologies like Dataproc, Google Kubernetes Engine, Cloud Storage and Google Network Load Balancing to grow its business.The impact was immediate. AppLovin saw latency on its bidding platform decrease by 25%, a material improvement to the customer experience. Network load balancers improved performance and provided ease of management. Savings like this, in money and brain power, mean AppLovin can continue to grow and scale. “Migrating to Google Cloud has been instrumental to our technology platform. The speed and strength of Google Cloud’s infrastructure and hardware has driven noticeable improvements across our business, and has given us a path to grow,” said Omer Hasan, VP of Operations at AppLovin.Like a good ML system, AppLovin’s technology continuously improves with more data. Its content investments mean even more of its first-party data will be coming onstream, increasing its data advantage, but underscoring the need for a strong and responsive compute infrastructure. ML can require an enormous amount of data across a system that is meant not just for scaling, but for changing—fast. This includes variable processing demands, rapid changes in values and configurations, and even automated system upgrades with minimal downtime or hassles. Speed. Power. Capability. Reliability. Partnering. Data insights, on an unmatched, secure network. If these are some of the things you’re looking for in a cloud, we’d love to see how we can help.Related ArticleRegistration is open for Google Cloud Next: October 12–14Register now for Google Cloud Next on October 12–14, 2021Read Article
Quelle: Google Cloud Platform

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Bridge data silos with Data Fusion

A huge challenge with data analytics is that the data is all over the place and is in different formats . As a result, you often need to complete numerous integration activities before you can start to gain insights from your data. Data Fusion offers a one-stop-shop for all enterprise data integration activities including ingestion, ETL, ELT and Streaming and with an execution engine optimized for SLAs and cost. It is designed to make lives easier for ETL developers, data analysts, and data engineers on Google Cloud, Hybrid Cloud or Multi Cloud environments. Click to enlargeData Fusion is Google’s cloud native, fully managed, scalable  enterprise data integration platform. It enables bringing transactional, social or machine data in various formats from databases, applications, messaging systems, mainframes, files, SaaS and IoT devices, , offers an easy to use visual interface , and provides deployment capabilities to execute data pipelines on ephemeral or dedicated Dataproc clusters in Spark. Cloud Data Fusion is  powered by open source CDAP which makes the pipelines portable across Google Cloud or Hybrid or multi cloud environments.. Data integration capabilities Data integration for optimized analytics and accelerated data transformationsData Fusion supports a broad set of more than 200 connectors and formats, which enables you to extract and blend data You can develop data  pipelines in a  visual environment to improve productivity. . Data Fusion provides data wrangling capabilities to  prepare data and provides capabilities to operationalize the data wrangling to improve business IT collaboration You can leverage the extensive REST API to design, automate, orchestrate and manage the lifecycle of the pipelines .Data Fusion supports all data delivery modes including batch, streaming or  real-time making it a comprehensive platform to address both batch and streaming related use cases.It provides operational insights so that you can monitor data integration processes. Manage SLA’s and help optimize and fine tune integration jobs. Data Fusion provides capabilities to parse and enrich unstructured data using Cloud AI, for example, converting audio files to text, applying NLP to detect sentiment, or extracting features from images and documents or converting HL7 to FHIR formatsData consistencyData Fusion builds confidence in business decision-making with advanced data consistency features: Data Fusion minimizes the risk of mistakes by providing structured ways of specifying transformations, data quality checks with Wrangler, and predefined directives.Data Fusion helps identify quality issues by keeping track of profiles of the data being integrated and enabling you make decisions based on data observability.Data formats change over time, Data Fusion helps handle data drift with the ability to identify change and customize error handling.Metadata and modelingData Fusion makes it easy to gain insights with metadata:You can collect technical, business, and operational metadata for datasets and pipelines and easily discover metadata with a search.Data Fusion, provides end-to-end data view to understand the data model, and to profile data, flows, and relationships of datasets.It enables exchange of metadata between catalogs and integration with end-user workbenches using REST APIs.The Data Fusion data lineage feature enables you to understand the flow of your data and how it is prepared for business decisions.Click to enlargeOpen, hybrid, and multi-cloudData Fusion is cloud-native and powered by CDAP, a 100% open-source framework for building on-premises and cloud data analytics applications . This means you can  deploy and execute integration pipelines in different environments without any changes to suit business needs. Data protectionData Fusion ensures data security in the following ways:It provides secure access to on-premises data with private IP.It encrypts data at rest by default or with Customer Managed Encryption Keys (CMEK) to control across all user data in supported storage systems.It provides data exfiltration protection via VPC Service Controls,  a security perimeter around platform resources.You can store sensitive passwords, URLs, and JDBC strings in Cloud KMS, and integrate with external KMS systems.It integrates with Cloud DLP to mask, redact, and encrypt data in transit.ConclusionChances are that in your enterprise there is data siloed in various platforms.  If it’s your job to bring it together, apply transformations, create data pipelines, and make all your data teams happier and more productive then Cloud Data Fusion has what you need. And if you already use Google Cloud data tools for curating a data lake with Cloud Storage and Dataproc, moving data into BigQuery for data warehousing, or transforming data for a relational store like Cloud Spanner, then Data Fusion integrations make development and iteration fast and easy. For a more in-depth look into Data Fusion check out the documentation.For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.Related ArticleBetter together: orchestrating your Data Fusion pipelines with Cloud ComposerSee how to orchestrate and manage ETL and ELT pipelines for data analytics in Cloud Composer using Data Fusion operators.Read Article
Quelle: Google Cloud Platform