Databricks Energy United States

SciPlay

Customer Service Automation · Enterprise Knowledge Search

To rise above the pack in the mobile gaming industry, it’s essential to give users superimmersive experiences that offer endless hours of entertainment. SciPlay, the mobile games company behind mega-hits like Jackpot Party and Quick Hit Casino, saw the need to enhance its data-driven approach to further engage customers and build games they’ll love.

5% Uplift in customer retention across all games
60x Faster time-to-insight for nontechnical teams
75% Faster time-to-market for new games and features
Internally, non-data teams benefit from vastly improved time-to-insight, which Databricks helped reduce from 4 hours to just 4 minutes — a 60x improvement.

Reported impact

Business outcome

In mobile gaming, winning is everything. SciPlay, creator of Jackpot Party, one of the top social casino games in the world, offers gameplay and the opportunity to beat the house every day to more than 6 million monthly users with games that are highly engaging, unquestionably secure and always dynamic.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth

Solution

SciPlay built its Databricks implementation around Delta Lake, Databricks Data + AI Platform, Databricks SQL, MLflow. To rise above the pack in the mobile gaming industry, it’s essential to give users superimmersive experiences that offer endless hours of entertainment. SciPlay, the mobile games company behind mega-hits like Jackpot Party and Quick Hit Casino, saw the need to enhance its data-driven approach to further engage customers and build games they’ll love. Delta Lake stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning. Databricks Data + AI Platform provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment. Databricks SQL serves governed analytics, dashboards, and ad hoc queries for business users and operational teams. MLflow tracks model experiments and repeatable machine learning workflows. In mobile gaming, winning is everything.

Data flow

Source data is ingested into databricks, curated in Delta Lake tables, processed with Databricks compute and orchestration services, and served through analytics, semantic search, or model endpoints for downstream users and applications.

Solution architecture

4 components · 4 layers
  1. Storage
    • Delta Lake Stores curated lakehouse tables with ACID guarantees for downstream analytics and machine learning.
  2. Serving
    • Databricks SQL Serves governed analytics, dashboards, and ad hoc queries for business users and operational teams.
  3. Orchestration
    • MLflow Tracks model experiments and repeatable machine learning workflows.
  4. Uncategorized
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workflows in this deployment.

Architecture clues

  • Databricks Data + AI Platform
  • Databricks SQL
  • Delta Lake
  • Faster time-to-insight for nontechnical teams
  • Improving user retention with real-time, actionable data modeling
  • MLflow

Evidence from the source

5% Uplift in customer retention across all games
75% Faster time-to-market for new games and features
Since SciPlay’s existing data warehouse required so much DevOps support, the overall infrastructure became a patchwork of tools and services left over from point solutions and acquisitions. Disconnected data silos blocked access to key info
With data and analytics fully democratized, SciPlay can now support various business teams from finance to marketing with reports and dashboards built in both Tableau and Databricks SQL. “With actionable information at our fingertips, nonte
“Data is one of our most precious assets to inform our decision-making,” said Ben-Meleh. “Coupled with our collective experience and knowledge, it’s a very powerful combination.
“The texture of our data is much better with lakehouse architecture,” said Ben-Meleh. “Conducting exploratory analysis between multiple data sources used to take several days — even just to gather the data prior to analysis would’ve taken t
Transparency Evidence & methodology 95/100 reference value · 94% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Databricks

    SciPlay customer story

    First-party
    https://www.databricks.com/customers/sciplay
    Source type
    Vendor-published customer story
    Published
    Feb 8, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. LeadIQ

    SciPlay Employee Directory, Headcount & Staff | LeadIQ

    Independent secondary
    https://leadiq.com/c/sciplay/5d28d3c16c628d5488265ebe/employee-directory

    LeadIQ says SciPlay has approximately 808 employees as of August 2026, and the Organization JSON-LD lists numberOfEmployees value 808 with addressCountry "United States."

    Source type
    Structured company profile
    Supports
    • Company size
    • Region

Date evidence

  • Published on source page Feb 8, 2023 · Databricks · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.