What are technical challenges Datazip can solve?

Data engineering can feel like juggling: ingesting massive datasets, ensuring data quality, managing pipelines, and controlling costs—all while delivering business insights quickly.

According to recent industry reports, over 70% of organizations struggle with building and maintaining data pipelines that can handle large volumes of data without breaking the bank.

At Datazip, we’ve built a unified data platform that tackles these challenges head-on, allowing teams to focus on insights rather than firefighting.

Below is a detailed table highlighting the biggest data engineering hurdles and the specific ways Datazip addresses them:

Data Challenge	Business/Technical Impact	Datazip Features	Implementation Steps	Direct Benefits
Hiring Data Engineers	- Talent Shortage: 1 available data engineer for every 15 companies - High Salaries: Overstretched budgets - Turnover Risk: Burned-out teams	- No Specialized Data Engineer Required - Self-Service UI & SQL-based transformations (dbt based)	1. Onboard an analyst or business user 2. Minimal training needed for using the Datazip UI 3. Access built-in guides & docs	- Reduced Hiring Costs - Faster Onboarding - Empower existing teams without massive skill-up
Massive Data Ingestion	- 100M+ Rows/Day - Missed SLAs if ETL tools fail under load - Pipeline Downtime - Unreliable Data Feeds for analytics	- Automated Ingestion Framework - Scales to 100–200M+ rows/day - Configurable frequency from 1 hr down to 1 min or 30 sec	1. Connect source systems (MySQL, Postgres, SaaS, files, etc. 150+ connectors.) 2. Set ingestion frequency 3. Monitor pipeline health in Datazip’s dashboard	- Stable High-Volume Pipelines - Real-time Insights with 1-min or 30-sec ingestion - Reduced Data Latency
Query Scalability Failures	- Production Overload - Timeouts & Failed Dashboards - Degraded App Performance - Inconsistent User Experience	- Scalable Warehouse with easy resource increases - Auto-Scaling on demand using OpenEngine	1. Choose initial warehouse size (or let Datazip advise) 2. Scale up/down via a simple UI	- Ensured High Concurrency - No More Query Failures - Reduced Manual Intervention
Data Quality & Accuracy	- Duplicates, Nulls, Stale Data - Eroded Trust in analytics if metrics don’t match reality - Costly Rework: Teams spend time fixing data repeatedly	- Test Case–Based Transformations (dbt) - Make data lineages and data models on the fly	1. Define rules for deduplication, null handling, stale data checks 2. Configure transformations in a no-code/low-code interface 3. Monitor quality	- Accurate, Consistent Metrics - Less Manual Cleanup - Faster Time to Insight
Data Governance & Secure Sharing	- Regulatory Compliance - Risk of Data Leaks or non-compliance (GDPR, HIPAA) - Hard to Collaborate securely among teams/3rd parties	- Role-Based Access Control (RBAC) - Row-Level Security - Data stays in your cloud so no need for GDPR	1. Create user roles & permissions via Datazip UI 2. Implement row-level security for sensitive datasets 3. Use audits/logs for compliance checks	- Controlled Data Exposure - Regulatory Peace of Mind - Safe Data Sharing within or across organizations
Multiple Tools & Vendors	- Complex Integrations & Pricing - Tool Sprawl: 4–5 different vendors (ingestion, warehousing, transformations, BI) - Integration Overhead & multiple negotiations	- All-in-One Data Platform - Unified pricing & functionality	1. Cancel or consolidate redundant tools 2. Migrate existing transformations & ingestion logic into Datazip 3. Use Datazip endpoints for BI tools	- Single Invoice - Streamlined Stack - Fewer Vendor Relationships
High Costs	- Rising with Data Growth - Budget Overruns: Egress fees, seat licenses, incremental storage - Reduced ROI from analytics	- Up to 60% Cost Efficiency vs. typical combos (Fivetran + DBT + Snowflake/BigQuery + Tableau)	1. Move ingestion, transformation, and storage into Datazip 2. Monitor usage & scale as needed 3. Eliminate additional third-party license fees	- Predictable Budget - Lower TCO - More Resources for high-impact initiatives

Common Challenges in Data Engineering Landscape

1. Shortage of Skilled Data Engineers

• Market Reality: There is roughly 1 data engineer available for every 15 companies looking to hire.

• Impact: Skyrocketing salaries, increased turnover, and overworked engineering teams.

Datazip’s Approach:

• No Data Engineer Needed: A single data analyst or business user with basic SQL knowledge can operate Datazip’s pipelines and transformations.

• User-Friendly UI: Self-service ingest, transform, and visualization endpoints cut the technical barrier to entry.

2. Creating & Maintaining High-Volume Data Pipelines

• Market Reality: Data volumes continue to grow exponentially, and many ETL (Extract, Transform, Load) solutions buckle under loads of 100+ million rows/day.

• Impact: Pipeline errors, missed SLAs, and engineering time spent scaling (or rewriting) ingestion processes.

Datazip’s Approach:

1. Automated Ingestion Framework: We handle anything from batch to near real-time ingestion (< 1-minute intervals).

2. Scalable Warehouse: Built on robust architectures designed to scale with minimal overhead.

3. Fault-Tolerant Processing: Intelligent retries and error-handling ensure data continues to flow.

3. Failing Queries Due to Scalability Issues

• Market Reality: Traditional databases (Postgres, MySQL, etc.) aren’t built for massive analytical workloads. Queries can time out or fail under load.

• Impact: Inconsistent dashboards, frustrated analysts, and inability to gain timely insights.

Datazip’s Approach:

• Scalable Warehouse at Your Fingertips: Seamlessly scale your Datazip warehouse with a few clicks.

• Roadmap for Auto-Scaling: Future releases will handle resource provisioning automatically, ensuring queries never fail due to lack of capacity.

4. Data Quality & Consistency

• Market Reality: Data can be incomplete, duplicated, or simply incorrect. According to Gartner, poor data quality costs organizations an average of $12.9 million annually.

• Impact: Wrong decisions, eroded trust in analytics, and repeated data clean-up tasks.

Datazip’s Approach:

• Test Case–Based Transformation Framework:

  • Quickly set rules for removing duplicates, handling nulls, or flagging stale data—no code required.

  • Built-in validations prevent bad data from silently flowing into reports.

• Unified Data Schema: Ensures consistency across different data sources.

5. Data Governance & Secure Sharing

• Market Reality: As data use scales, controlling who sees what becomes critical—especially with GDPR, HIPAA, and other regulations.

• Impact: Risk of data leaks, compliance breaches, and complicated user provisioning.

Datazip’s Approach:

• Role-Based Access Control (RBAC): Assign fine-grained permissions per team or individual.

• Row-Level Security: Share only the relevant subset of data without exposing sensitive records.

• Audit Trails: Track changes, data access logs, and queries for full transparency.

6. Juggling Multiple Tools, Vendors, and Pricing Models

• Market Reality: Many data stacks have 4–5 separate vendors (Fivetran, DBT, Snowflake, Looker/Tableau, etc.).

• Impact: Complex negotiations, varied pricing structures, potential integration issues, and vendor lock-in.

Datazip’s Approach:

• One Tool, One Pricing Strategy:

  • Datazip unifies ingestion, transformation, storage, and analytics endpoints under one roof.

  • Streamlined billing; no dealing with multiple licenses or vendor negotiations.

7. Ballooning Costs of Existing Data Setup

• Market Reality: Costs often spiral out of control as data volume increases. Egress fees, add-on features, and seat licenses accumulate.

• Impact: CFO/CTO friction, reduced ROI from analytics, stifled innovation due to budget constraints.

Datazip’s Approach:

• 60% Cost Savings: Compared to the combined cost of popular tools like Fivetran + DBT + Snowflake/BigQuery + Tableau, Datazip is significantly more affordable.

• Predictable Pricing: Transparent, usage-based or subscription-based models (depending on your needs).

Step-by-Step: How Datazip Streamlines Your Data Pipeline

Below is a high-level roadmap for teams adopting Datazip:

1. Source Onboarding

   • Connect your databases (Postgres, MongoDB, etc.) or SaaS platforms.

   • Configure ingestion frequency (ranging from 1 hour to as near real-time as 30 seconds).

2. Data Transformation & Validation

   • Define test-based rules to clean and standardize your data.

   • Instantly detect duplicates, null fields, or inconsistent records.

3. Warehouse Configuration

   • Choose your initial warehouse size or let Datazip’s recommended settings guide you.

   • Future auto-scaling capability will grow or shrink resources based on current load.

4. Data Governance Setup

   • Create roles and permissions via our RBAC framework.

   • Enable row-level security if different teams (or external partners) need restricted access.

5. BI Tool Integration

   • Point your existing visualization tool (Tableau, Metabase, Superset, Power BI, Redash, etc.) to Datazip’s endpoint.

   • Enjoy sub-second or near sub-second queries without overloading production databases.

6. Monitoring & Alerts

   • Built-in dashboards to track ingestion volume, pipeline health, query performance, and costs.

   • Get alerts for pipeline failures, anomalies, or sudden cost spikes.

Advanced Issues and How Datazip Helps

1. Schema Evolution:

   • Problem: Changing data structures can break pipelines.

   • Solution: Datazip auto-detects schema changes and can intelligently adapt transformations without manual re-configuration.

2. Concurrency & High User Loads:

   • Problem: Many tools degrade in performance when multiple users run heavy queries simultaneously.

   • Solution: Datazip’s columnar storage and distributed architecture handle high concurrency with minimal slowdowns.

3. Historical vs. Real-Time Analysis:

   • Problem: Combining large historical datasets with fresh, real-time data can be complex.

   • Solution: Datazip’s ingestion framework easily merges streaming (near real-time) data with historical data, ensuring a unified, comprehensive data view.

Numbers that Matter

• 100M–200M+ rows/day ingestion capacity—easily scalable for larger volumes.

• 60% cost savings compared to typical multi-vendor data stacks.

• 1-minute intervals for near real-time ingestion.

• Zero (0) specialized engineers needed—allowing data analysts to manage the bulk of the pipeline.

Datazip is purpose-built for organizations seeking a high-performance, cost-effective solution that removes complexity from the modern data stack. Whether you’re ingesting millions (or hundreds of millions) of rows per day, needing stringent data governance, or simply tired of the ever-growing vendor list—Datazip has you covered.

Ready to see it in action?

1. Book a Demo: Experience how Datazip can simplify your analytics pipeline and reduce overall costs.

2. Contact Us: Drop us a line at hello@datazip.io, and let’s chat about your specific data challenges.

Stop wasting time juggling multiple tools and wrestling with data pipelines. Empower your teams with Datazip’s unified approach and unlock the full potential of your data, no data engineering army required.