Modernizing Your Data Analytics Architecture For the Cloud

There is an explosion of data from a myriad of sources and an insatiable demand to consume it. Traditional manual ETL methods are too brittle to keep up. Leaving many a BI team struggling to provide meaningful business insights quickly.

According to a recent TDWI Pulse report, “Modernizing Your Data Architecture to Unlock Business Value,” transformation and preparation processes are slow; more than half (57%) of organizations surveyed by TDWI say it takes more than a day and up to a full month to add new data to BI and analytics platforms, and 14% say it takes longer than one month.

Coupled with the challenges of getting access to data for analytics, especially from mission critical systems that are running the business without impacting performance of these production systems practically grinds the data delivery process to a halt. More than half (55%) of TDWI survey respondents regard utilizing data from mainframe/legacy applications as the most difficult, with 22% calling it “very difficult.” A significant percentage (43%) find it difficult in utilizing data from on-premises ERP systems, such as SAP and Oracle.

But it’s not just internal data there is much and more data available to organizations. Data thriving in an array of heterogeneous systems containing valuable insights waiting to be tapped. Interweaving these systems could prove invaluable to growth.

However, limitations on user agility and any increased latency in interacting with relevant data have significant business impacts. TDWI state that businesses need an architecture that not only enables users to generate insights on their own but can also adapt to the ever-changing data landscape and cater for the unknow.

This is why the emphasis is on the ability to keep your data in motion and analyze the freshest data available, so you can react to what is happening in your business within the moment and take action in real-time. To do this TDWI recommends that organizations should look to build in the cloud and evaluate the use of lambda and kappa architectures.

Qlik is a cloud first company, an important part of this is close strategic partnerships with leading tech vendors like AWS. We are proud to achieved Amazon Web Services (AWS) Migration & Modernization Competency status for AWS Partners. This designation recognizes that Qlik has demonstrated technical proficiency and proven customer success automating and accelerating customer application migration and modernization journeys.

The AWS Migration & Modernization Competency is testament to Qlik’s continued work with AWS, centered around maximizing the value of analytics as a service for AWS customers.

Qlik also holds a variety of AWS designations, including.

  • AWS Advanced Technology partner
  • Amazon Redshift Ready designation for data analytics
  • Amazon Relational Database Service (RDS) Service Ready

We have a broad depth of integrations and designations, from Qlik solutions listed in AWS Marketplace to a newly announced Amazon SageMaker integration, resulting in customers confidently deploying AWS technology alongside Qlik to drive more value from all their data in the cloud.

The TDWI Pulse report outlines six key recommendations to unlock the business value of data, let’s look at how these can align with Qlik and AWS.

  1. Take advantage of the cloud - Qlik can help you automate your data delivery to multiple AWS services, from storage, data warehouse to streaming systems.
  2. Improve access to enterprise data - Qlik Data Integration is built on log-based change data capture (CDC), enabling transactional data to be moved to AWS cloud in a consistence way.
  3. Integrate cloud data lakes and warehouses - In addition Qlik Data Integration platform automates the creation and management of Data Warehouses and Data Lakes such as Redshift, Snowflake and Databricks, all without any coding, significantly accelerating time of data prep.
  4. Handle real-time data use cases - log-based CDC plays an important role in moving data to cloud as quickly as possible with low latency. Qlik can help you automate streaming data pipelines from heterogeneous sources including mainframe and SAP systems, opening up use cases such as modernizing applications analytics to new app development.
  5. Establish unified metadata repository - Qlik Data Integration includes Qlik Catalog enabling the Data Engineer to quality check data, the Data Steward to tag, protect and mask sensitive data, and the business user find it and publish it to their tool of choice BI or Data Science tool.
  6. Self-service analytics - Qlik offers Data Analytics independently of Qlik Data Integration but when used together you make your data more accessible with self service capabilities and drive better, quicker, insights to the rest of the business in secure and governed ways, going beyond purely visualizing and opening use cases from cataloguing, automation workflows between applications and alerting to drive immediate informed decisions and actions.

Achieving the AWS Migration & Modernization Competency differentiates Qlik as an AWS Partner with deep domain expertise delivering software products that help customers embrace cloud and application transformation, reducing licensing costs, optimizing operational costs, and improving performance, agility, and resiliency. Qlik and AWS can help you perform an application portfolio assessment, identifying the applications that are candidates for modernization and through the Qlik Data Integration platform augment and automate developer tasks to carry out the modernization of legacy applications.

Download the TWDI Pulse report and watch this related webinar to learn more on this topic.

And our very own Dan Potter presented on this very topic during AWS re:Invent on 30th November; stay tuned for the on-demand version coming soon.

@TDWI Pulse Report prescribes modernizing your #data architecture in the cloud; @adammayerwrk explores how AWS & Qlik can help you do it

 

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