How smava makes loans clear and reasonably priced utilizing Amazon Redshift Serverless

It is a visitor submit co-written by Alex Naumov, Principal Knowledge Architect at smava.

smava GmbH is without doubt one of the main monetary providers firms in Germany, making private loans clear, honest, and reasonably priced for customers. Primarily based on digital processes, smava compares mortgage presents from greater than 20 banks. On this means, debtors can select the offers which can be most favorable to them in a quick, digitalized, and environment friendly means.

smava believes in and takes benefit of data-driven selections in an effort to develop into the market chief. The Knowledge Platform group is chargeable for supporting data-driven selections at smava by offering knowledge merchandise throughout all departments and branches of the corporate. The departments embrace groups from engineering to gross sales and advertising and marketing. Branches vary by merchandise, specifically B2C loans, B2B loans, and previously additionally B2C mortgages. The information merchandise used inside the corporate embrace insights from person journeys, operational reviews, and advertising and marketing marketing campaign outcomes, amongst others. The information platform serves on common 60 thousand queries per day. The information quantity is in double-digit TBs with regular progress as enterprise and knowledge sources evolve.

smava’s Knowledge Platform group confronted the problem to ship knowledge to stakeholders with totally different SLAs, whereas sustaining the pliability to scale up and down whereas staying cost-efficient. It took as much as 3 hours to generate each day reporting, which impacted enterprise decision-making when re-calculations wanted to occur throughout the day. To hurry up the self-service analytics and foster innovation based mostly on knowledge, an answer was wanted to offer methods to permit any group to create knowledge merchandise on their very own in a decentralized method. To create and handle the info merchandise, smava makes use of Amazon Redshift, a cloud knowledge warehouse.

On this submit, we present how smava optimized their knowledge platform through the use of Amazon Redshift Serverless and Amazon Redshift knowledge sharing to beat right-sizing challenges for unpredictable workloads and additional enhance price-performance. By the optimizations, smava achieved as much as 50% price financial savings and as much as 3 times quicker report era in comparison with the earlier analytics infrastructure.

Overview of answer

As a data-driven firm, smava depends on the AWS Cloud to energy their analytics use instances. To convey their prospects one of the best offers and person expertise, smava follows the trendy knowledge structure ideas with a knowledge lake as a scalable, sturdy knowledge retailer and purpose-built knowledge shops for analytical processing and knowledge consumption.

smava ingests knowledge from numerous exterior and inside knowledge sources right into a touchdown stage on the info lake based mostly on Amazon Easy Storage Service (Amazon S3). To ingest the info, smava makes use of a set of well-liked third-party buyer knowledge platforms complemented by customized scripts.

After the info lands in Amazon S3, smava makes use of the AWS Glue Knowledge Catalog and crawlers to mechanically catalog the out there knowledge, seize the metadata, and supply an interface that enables querying all knowledge property.

Knowledge analysts who require entry to the uncooked property on the info lake use Amazon Athena, a serverless, interactive analytics service for exploration with advert hoc queries. For the downstream consumption by all departments throughout the group, smava’s Knowledge Platform group prepares curated knowledge merchandise following the extract, load, and rework (ELT) sample. smava makes use of Amazon Redshift as their cloud knowledge warehouse to remodel, retailer, and analyze knowledge, and makes use of Amazon Redshift Spectrum to effectively question and retrieve structured and semi-structured knowledge from the info lake utilizing SQL.

smava follows the knowledge vault modeling methodology with the Uncooked Vault, Enterprise Vault, and Knowledge Mart phases to organize the info merchandise for finish customers. The Uncooked Vault describes objects loaded instantly from the info sources and represents a replica of the touchdown stage within the knowledge lake. The Enterprise Vault is populated with knowledge sourced from the Uncooked Vault and reworked based on the enterprise guidelines. Lastly, the info is aggregated into particular knowledge merchandise oriented to a particular enterprise line. That is the Knowledge Mart stage. The information merchandise from the Enterprise Vault and Knowledge Mart phases at the moment are out there for customers. smava determined to make use of Tableau for enterprise intelligence, knowledge visualization, and additional analytics. The information transformations are managed with dbt to simplify the workflow governance and group collaboration.

The next diagram exhibits the high-level knowledge platform structure earlier than the optimizations.

High-level Data Platform architecture before the optimizations

Evolution of the info platform necessities

smava began with a single Redshift cluster to host all three knowledge phases. They selected provisioned cluster nodes of the RA3 kind with Reserved Cases (RIs) for price optimization. As knowledge volumes grew 53% 12 months over 12 months, so did the complexity and necessities from numerous analytic workloads.

smava shortly addressed the rising knowledge volumes by right-sizing the cluster and utilizing Amazon Redshift Concurrency Scaling for peak workloads. Moreover, smava needed to offer all groups the choice to create their very own knowledge merchandise in a self-service method to extend the tempo of innovation. To keep away from any interference with the centrally managed knowledge merchandise, the decentralized product growth environments wanted to be strictly remoted. The identical requirement was additionally utilized for the isolation of various product phases curated by the Knowledge Platform group.

Optimizing the structure with knowledge sharing and Redshift Serverless

To fulfill the advanced necessities, smava determined to separate the workload by splitting the one provisioned Redshift cluster into a number of knowledge warehouses, with every warehouse serving a distinct stage. As well as, smava added new staging environments within the Enterprise Vault to develop new knowledge merchandise with out the danger of interfering with present product pipelines. To keep away from any interference with the centrally managed knowledge merchandise of the Knowledge Platform group, smava launched a further Redshift cluster, isolating the decentralized workloads.

smava was in search of an out-of-the-box answer to attain workload isolation with out managing a fancy knowledge replication pipeline.

Proper after the launch of Redshift knowledge sharing capabilities in 2021, the Knowledge Platform group acknowledged that this was the answer they’d been in search of. smava adopted the info sharing characteristic to have the info from producer clusters out there for learn entry on totally different client clusters, with every of these client clusters serving a distinct stage.

Redshift knowledge sharing permits immediate, granular, and quick knowledge entry throughout Redshift clusters with out the necessity to copy knowledge. It supplies dwell entry to knowledge in order that customers all the time see essentially the most up-to-date and constant data because it’s up to date within the knowledge warehouse. With knowledge sharing, you’ll be able to securely share dwell knowledge with Redshift clusters in the identical or totally different AWS accounts and throughout Areas.

With Redshift knowledge sharing, smava was capable of optimize the info structure by separating the info workloads to particular person client clusters with out having to duplicate the info. The next diagram illustrates the high-level knowledge platform structure after splitting the one Redshift cluster into a number of clusters.

High-level Data Platform architecture after splitting the single Redshift cluster in multiple clusters

By offering a self-service knowledge mart, smava elevated knowledge democratization by offering customers with entry to all points of the info. Additionally they supplied groups with a set of customized instruments for knowledge discovery, advert hoc evaluation, prototyping, and working the complete lifecycle of mature knowledge merchandise.

After gathering operational knowledge from the person clusters, the Knowledge Platform group recognized additional potential optimizations: the Uncooked Vault cluster was below regular load 24/7, however the Enterprise Vault clusters have been solely up to date nightly. To optimize for prices, smava used the pause and resume capabilities of Redshift provisioned clusters. These capabilities are helpful for clusters that should be out there at particular occasions. Whereas the cluster is paused, on-demand billing is suspended. Solely the cluster’s storage incurs expenses.

The pause and resume characteristic helped smava optimize for price, but it surely required extra operational overhead to set off the cluster operations. Moreover, the event clusters remained topic to idle occasions throughout working hours. These challenges have been lastly solved by adopting Redshift Serverless in 2022. The Knowledge Platform group determined to maneuver the Enterprise Knowledge Vault stage clusters to Redshift Serverless, which permits them to pay for the info warehouse solely when in use, reliably and effectively.

Redshift Serverless is right for instances when it’s troublesome to foretell compute wants akin to variable workloads, periodic workloads with idle time, and steady-state workloads with spikes. Moreover, as utilization demand evolves with new workloads and extra concurrent customers, Redshift Serverless mechanically provisions the fitting compute sources, and the info warehouse scales seamlessly and mechanically, with out the necessity for guide intervention. Knowledge sharing is supported in each instructions between Redshift Serverless and provisioned Redshift clusters with RA3 nodes, so no modifications to the smava structure have been wanted. The next diagram exhibits the high-level structure setup after the transfer to Redshift Serverless.

High-level Data Platform architecture after introducing Redshift Serverless for Business Vault clusters

smava mixed the advantages of Redshift Serverless and dbt by means of a seamless CI/CD pipeline, adopting a trunk-based growth methodology. Adjustments on the Git repository are mechanically deployed to a take a look at stage and validated utilizing automated integration checks. This method elevated the effectivity of builders and decreased the common time to manufacturing from days to minutes.

smava adopted an structure that makes use of each provisioned and serverless Redshift knowledge warehouses, along with the info sharing functionality to isolate the workloads. By selecting the best architectural patterns for his or her wants, smava was capable of accomplish the next:

  • Simplify the info pipelines and cut back operational overhead
  • Cut back the characteristic launch time from days to minutes
  • Enhance price-performance by decreasing idle occasions and right-sizing the workload
  • Obtain as much as 3 times quicker report era (quicker calculations and better parallelization) at 50% of the unique setup prices
  • Enhance agility of all departments and assist data-driven decision-making by democratizing entry to knowledge
  • Enhance the pace of innovation by exposing self-service knowledge capabilities for groups throughout all departments and strengthening the A/B take a look at capabilities to cowl the entire buyer journey

Now, all departments at smava are utilizing the out there knowledge merchandise to make data-driven, correct, and agile selections.

Future imaginative and prescient

For the longer term, smava plans to proceed to optimize the Knowledge Platform based mostly on operational metrics. They’re contemplating switching extra provisioned clusters just like the Self-Service Knowledge Mart cluster to serverless. Moreover, smava is optimizing the ELT orchestration toolchain to extend the variety of parallel knowledge pipelines to be run. This may improve the utilization of provisioned Redshift sources and permit for price reductions.

With the introduction of the decentralized, self-service for knowledge product creation, smava made a step ahead in the direction of a knowledge mesh structure. Sooner or later, the Knowledge Platform group plans to additional consider the wants of their service customers and set up additional knowledge mesh ideas like federated knowledge governance.


On this submit, we confirmed how smava optimized their knowledge platform by isolating environments and workloads utilizing Redshift Serverless and knowledge sharing options. These Redshift environments are nicely built-in with their infrastructure, versatile in scaling on demand, and extremely out there, they usually require minimal administration efforts. General, smava has elevated efficiency by 3 times whereas decreasing the overall platform prices by 50%. Moreover, they lowered operational overhead to a minimal whereas sustaining the present SLAs for report era occasions. Furthermore, smava has strengthened the tradition of innovation by offering self-service knowledge product capabilities to hurry up their time to market.

In case you’re fascinated by studying extra about Amazon Redshift capabilities, we suggest watching the latest What’s new with Amazon Redshift session within the AWS Occasions channel to get an summary of the options not too long ago added to the service. You too can discover the self-service, hands-on Amazon Redshift labs to experiment with key Amazon Redshift functionalities in a guided method.

You too can dive deeper into Redshift Serverless use instances and knowledge sharing use instances. Moreover, try the knowledge sharing finest practices and uncover how different prospects optimized for price and efficiency with Redshift knowledge sharing to get impressed to your personal workloads.

In case you favor books, try Amazon Redshift: The Definitive Information by O’Reilly, the place the authors element the capabilities of Amazon Redshift and give you insights on corresponding patterns and strategies.

In regards to the Authors

Blog author: Alex NaumovAlex Naumov is a Principal Knowledge Architect at smava GmbH, and leads the transformation initiatives on the Knowledge division. Alex beforehand labored 10 years as a marketing consultant and knowledge/answer architect in all kinds of domains, akin to telecommunications, banking, power, and finance, utilizing numerous tech stacks, and in many various nations. He has an awesome ardour for knowledge and reworking organizations to develop into data-driven and one of the best in what they do.

Blog author: Lingli ZhengLingli Zheng works as a Enterprise Improvement Supervisor within the AWS worldwide specialist group, supporting prospects within the DACH area to get one of the best worth out of Amazon analytics providers. With over 12 years of expertise in power, automation, and the software program trade with a give attention to knowledge analytics, AI, and ML, she is devoted to serving to prospects obtain tangible enterprise outcomes by means of digital transformation.

Blog author: Alexander SpivakAlexander Spivak is a Senior Startup Options Architect at AWS, specializing in B2B ISV prospects throughout EMEA North. Previous to AWS, Alexander labored as a marketing consultant in monetary providers engagements, together with numerous roles in software program growth and structure. He’s enthusiastic about knowledge analytics, serverless architectures, and creating environment friendly organizations.

This submit was reviewed for technical accuracy by David Greenshtein, Senior Analytics Options Architect.

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