As the urgency to compete on analytics continues to revolutionize the business world, more and more organizations are moving their data to the cloud to reduce infrastructure costs, increase efficiencies and improve time-to-value.
At the same time, there are many success factors to consider, from the strengths and weaknesses of different cloud providers, to integration hurdles, data latency challenges and governance problems.
DBTA recently had a webinar featuring Clive Bearman, director of product marketing, Attunity; Pradeep Bhanot, director product marketing, Actian; and Brian Hess, strategic solution engineer, Datastax, who discussed insights and advice to help facilitate cloud journeys.
According to Bearman, there are three market trends: AWS, Azure, and Google Cloud platform shows that there continues to be a massive adoption of cloud. Multi-cloud is not uncommon in larger enterprises.
The next trend sees data warehousing making a comeback with Snowflake, Google Big Query, and more leading the pack. The last trend is the emergence of new platforms, including streaming architectures.
There are several data integration methods to choose from that includes ETL, API, and CDC, Bearman said.
The Attunity solution enables DataOps for analytics, Bearman explained. The platform is architected for real-time change data capture and analytics ready data delivery. It can move real-time data between relational, mainframe, Big Data, EDW, cloud, file systems and applications with one pipeline solution.
It also provides end to end automation that includes target table creation, mappings, schema synchronization, EDW/Data Mart and data lake creation, management and documentation.
Legacy enterprise data warehouses hinder business growth, Bhanot said. It is very hard to add new data sources, is architected for data and user volumes of more than a decade ago, and as systems reach capacity expanding the environment is expensive.
Actian Avalanche can deliver real-time insights to business decision makers, Bhanot noted.
Actian Avalanche hybrid cloud data warehouse includes:
- Innovative architecture
- 10 – 20x performance at 50% lower TCO
- Is a hybrid solution that enables the cloud journey
Hess listed considerations for operational analytics that enterprises should be looking at including:
- Address the data half-life: immediate, automatically synchronized data
- Operationalize analytics: immediately and seamlessly serve results to applications/users. Derive real-time business value from insights
- Workload isolation: analytics does not impact transactions. Make the DBA happy.
- Simplicity: transactions and analytics
An archived on-demand replay of this webinar is available here.