The explosion in data, the vast array of new capabilities, and the dramatic increase in demands have changed how data needs to be moved, stored, processed, and analyzed. But new architectures like data warehouses and lakes are creating additional bottlenecks within IT because many existing processes are labor-intensive and insufficient.
Strategies to Automate and Accelerate End-To-End Data Pipeline
How can you satisfy today’s real-time data requirements while keeping data traceable and governed? By taking advantage of four key strategies to automate and accelerate your end-to-end data pipeline. Read on this article for an introduction, with topics including:
- The use of change data capture technology to propagate real-time changes
- The automation of data warehouse and lakes to rapidly add trusted data
- The role of an enterprise data catalog in making data accessible
Content Summary
New data demands are inspiring new architectures
New data architectures are creating new challenges
A new approach to data integration: DataOps for Analytics
Top 4 strategies for meeting today’s integration challenges
Strategy 1: Use change data capture
Strategy 2: Automate the creation of data warehouses
Strategy 3: Automate the creation of data lakes
Strategy 4: Build and employ an enterprise data catalog
The Result Are Real
Qlik’s modern Data Integration Platform
Qlik’s platform at a glance
Accelerate business value with data
New data demands are inspiring new architectures
The explosion in data, the vast array of new data capabilities, and the dramatic increase in data-consumer demands have changed how data needs to be moved, stored, processed, and analyzed.
Today’s businesses need an architecture that scales easily automates data integration processes, and streams data in real-time. As a result, more and more organizations are:
- Investing in and moving data to the cloud
- Accelerating and simplifying the data warehouse and data lake lifecycle
- Replacing inefficient batch replication processes with real-time data streaming
New data architectures are creating new challenges
This new environment has created additional complexity and bottlenecks within IT because many existing processes and technologies are insufficient. In today’s landscape, reliable, real-time data delivery requires:
- Integration: Bringing together increasingly high volumes of data from an increasing array of sources and replicating it to analytics platforms without disrupting production applications
- Governance: Tracking, maintaining and protecting data at every stage of the lifecycle
- Agility: Automating the design and refinement of data warehouses and data lakes while leveraging best practices
A new approach to data integration: DataOps for Analytics
There’s an emerging strategy that enables a modern, comprehensive approach to meet today’s data demands: DataOps for Analytics.
Borrowing methods from the DevOps concept, which combines software development and IT operations to improve the velocity, quality, predictability, and scale of software development and deployment, DataOps seeks to bring similar improvements with delivering data for analytics. It focuses on the practices, processes, and technologies for building and enhancing data pipelines to quickly meet business needs.
Technology + Processes + People = DataOps
DataOps isn’t a product or a software platform; it’s a methodology. Technology is a vital component, but it’s only one component. You’ll also need to rework the operational aspects of your data supply chain and bring your workforce along.
Top 4 strategies for meeting today’s integration challenges
You can meet today’s agility and real-time data requirements by leveraging DataOps for Analytics to automate and accelerate your data supply chain. These four strategies get data flowing faster.
Strategy 1: Use change data capture
Use change data capture to identify and propagate data changes as they occur
Capabilities: Real-time streaming, Replication, Efficient cloud delivery
Benefits:
- Continuously replicate data by identifying and copying data updates as they take place
- Keep users informed about where the data came from, where it’s been, and how it’s changed along the way
Strategy 2: Automate the creation of data warehouses
Automate the creation of data warehouses for the rapid addition of new data sources and the creation of purpose-built data marts
Capabilities: Automated ETL generation, Self-service marts, Cloud optimization
Benefits:
- Empower data delivery teams to easily convert raw data into a governed, analytics-aware resource
- Give unique business units or functions faster access to relevant data within the data warehouse, speeding time-to-insight in a cost-effective way
Strategy 3: Automate the creation of data lakes
Automate the creation of data lakes to provide continuously updated, accurate and trusted data sets
Capabilities: Real-time data ingestion, Automated, continuous refinement Trusted, enterprise-ready data
Benefits:
- Quickly and easily create high-scale data pipelines
- Remove as much scripting as possible, adapting multistage data processing without coding
- Close the “last mile” by provisioning analytics-ready data in real-time
Strategy 4: Build and employ an enterprise data catalog
Build and employ an enterprise data catalog to make every new data set available and accessible
Capabilities: Automated profiling and transformation, Data lineage Sensitive data encryption, On-demand access
Benefits:
- Provide enterprise-wide visibility into siloed data sources to make business-ready data available on demand
- Empower users to find, reuse, comment on and share data sets throughout a smart data catalog
- Track data usage and protect, enforce and monitor data access policies throughout the data lifecycle
- Deliver any data to any BI tool or application
The Result Are Real
By 2021, organizations that offer a curated catalog of internal and external data to diverse users will realize twice the business value from their data and analytics investments than those that do not.
Gartner, “Augmented Data Catalogs: Now an Enterprise Must-Have for Data and Analytics Leaders,” September 2019. Authored by Ehtisham Zaidi and Guido De Simoni.
Qlik’s modern Data Integration Platform
Closing the gap between relevant data and actionable data. Business users need to be confident that the data they analyze is accurate, safe, and verifiable. Qlik’s Data Integration Platform includes a robust set of enterprise-scale quality, governance, and collaboration capabilities to vastly accelerate the discovery and availability of real-time, analytics-ready data.
Our modern data integration platform comprises:
- Real-time data streaming with change data capture: Extend enterprise data into live streams to enable modern analytics and microservices with a simple, real-time and universal solution
- Agile data warehouse automation: Quickly design, build, deploy and manage purpose-built data warehouses without manual coding
- Managed data lake creation: Automate complex ingestion and transformation processes to provide continuously updated and analytics-ready data lakes
- A smart, integrated data catalog – for trusted, governed data: Give data consumers a reliable, intuitive way to access, find, understand and self-provision data
Built to move data into ant BI Platform: Qlik’s Data Integration Platform is architected for today’s data landscape. It works with all data consumers, including BI offerings from Qlik, Tableau, Microsoft, and others.
Qlik’s platform at a glance
Stream all your organization’s data, from any source, through automated and governed pipelines to the analytics applications of your choice. Fast.
Qlik’s platform at a glance
Accelerate business value with data
Use data integration to propel your business forward. The ultimate goal of any data project is to help the business innovate, transform, succeed, compete, and grow. At Qlik, we’re committed to business value acceleration, supporting you across the entire data pipeline so you can take raw data from any source and transform it into insights that matter.
Source: Qlik Technologies Inc
