Salesforce Data Import Without Backup Plan is a Disaster

Stop wasting time on manual data exports and complex custom coding. In this quick demo, we show how DBSync automates the replication process between Salesforce and SQL Server, keeping your backend and CRM in perfect sync. Why it matters: - Zero Code: Set up your integration in minutes. - Accuracy: Eliminate manual data entry errors. - Efficiency: Focus on insights, not infrastructure. Check out the full demo here: https://lnkd.in/daGhYVf5 #DBSync #Salesforce #SQLServer #DataIntegration #Automation #ProductManagement

📌Why SOQL Works in Sandbox; but Fails in Production I’ve seen this many times as a salesforce professional. A query works perfectly in Sandbox: → Small data → Fast results Then it hits Production: → Timeouts → “Non-selective query” errors → Slow reports Nothing broke. The data changed. 🔍 The real issue? Query selectivity Salesforce only runs queries efficiently if filters reduce the dataset enough. If not → full table scan → performance issues ✅Rule of thumb: → Standard index: < 30% of records → Custom index: < 10% ⚠️ Common mistakes: i.Filtering on non-indexed fields ii.Using NOT / != / leading wildcards iii.Testing only in Sandbox 🚀 What I focus on: ✅ Use indexed fields ✅ Combine filters ✅ Plan for data growth ✅ Request indexes early Insight: A query that works today can fail tomorrow. SOQL performance is about data scale, not syntax. 💬 Have you faced this in Production? #Salesforce #SOQL #SalesforceAdmin #LDV #ContinuousLearnig #SalesforceBA #Data

🚀 **Day 12 of Learning Salesforce** Day 12 was focused on understanding **SOQL (Salesforce Object Query Language)** and how to retrieve data efficiently. 🔍 **What I learned today:** * Introduction to **SOQL** * Writing basic **SELECT queries** to fetch data * Using **WHERE conditions** to filter records * Understanding how SOQL is different from SQL It was interesting to see how querying works within Salesforce and how easily we can retrieve specific data when needed. 💡 Excited to practice more complex queries and explore SOSL next! #Salesforce #LearningJourney #CRM #SOQL #DataQuery #CloudComputing #Day12 #CareerGrowth

🚨 “Too many query rows: 50001” One of the most common Apex errors in Salesforce! Almost every Salesforce Developer faces this error while working on Triggers, Batch Jobs, Integrations, or Large Data Volume scenarios. But many developers still struggle to fix it properly in production environments. That’s why we created this detailed guide. ⚠️ Quick Check for Salesforce Developers: 1️⃣ What actually causes Too Many Query Rows 50001 error? 2️⃣ Why does it appear even when SOQL looks correct? 3️⃣ How will you handle this in bulk triggers? 4️⃣ Can Batch Apex completely solve this issue? 5️⃣ What is the role of SOQL for loop here? 6️⃣ How will you manage large data volume in production? 7️⃣ What are the best practices to avoid this error permanently? If you are not confident with these, this article is for you. 🔥 New Article Published ✔ What is Too Many Query Rows Error ✔ Why it happens ✔ Real-world Apex scenarios ✔ Production-safe solutions ✔ Best practices and optimization tips 🔗 Read the full article here: https://lnkd.in/gkhUBFTX 💬 Question for Salesforce Developers: 🤝 This is a collaboration article by TriggerHours published on AyanInsights to help Salesforce developers understand and resolve this issue with real-world solutions. --------------------------------------------------------------

❌ “No SOQL in loop” 💥 Still hit Too many SOQL queries: 201 in Production. Yes — this actually happened. Everything looked clean: ✔ Bulkified logic ✔ No queries in loops ✔ Async processing in place Still… it failed. 🔍 Root Cause? The loop was clean. But the execution path wasn’t. 👉 A service method was called inside the loop 👉 That method contained hidden SOQL queries 💣 Result: indirect queries per record → governor limit exceeded 🧠 Biggest Lesson: You don’t just review code. You review the entire execution path. 💡 What I changed: ✔ Moved queries outside loops ✔ Built Map-based data access ✔ Passed data into services (no DB calls inside) ✔ Used Custom Metadata for dynamic logic 🚀 Outcome: ⚡ Zero failures 📈 Faster execution 🧩 Truly scalable design 📌 Final Thought: Hidden SOQL is often more dangerous than visible SOQL. #Salesforce #Apex #GovernorLimits #Bulkification #QueueableApex #BatchApex #SalesforceDeveloper #CRM

Salesforce Random Notes #8: Why Salesforce gives you 50,000 records to query but only 10,000 to update. Ever felt like the SOQL limits were a bit of a "trap"? I was diving deep into data strategy today and hit that classic question: If I can query 50,000 records in one go, why does the system crash the moment I try to update 30,000 of them? Here’s the breakdown for my fellow #Salesforce developers and architects: 1. Read vs. Write Cost: Reading data is "cheap," but writing is "expensive." When you hit "Update," you aren't just changing a cell. You’re triggering a chain reaction of Flows, Triggers, and Validation Rules. The 10k DML limit is there to protect the multi-tenant "engine" from overheating! 2. The "Semi-Join" Surprise: Coming from a SQL background? Watch out! Standard SOQL doesn't support GROUP BY or HAVING inside a subquery. If you need to filter Accounts based on an aggregate count of Contacts, you’ll need to bridge the gap with Apex or a Roll-up Summary field. 3. Scaling for LDV (Large Data Volumes): Moving 100k+ records? ❌ Standard Synchronous Apex will fail. ✅ Batch Apex is your best friend—breaking that 30k update into 150 manageable "chunks" of 200 records. The Golden Rule: • SOQL (50k) is for Analysis. • DML (10k) is for Action. • Batch Apex is for Mass Migration. The platform isn't limiting us; it’s forcing us to build for scale. #SalesforceDeveloper #Apex #SOQL #SalesforceOhana #DataMigration 

I understood how data worked before I even knew what Salesforce was. In a previous role, I worked directly with SQL (primarily data updates and cleanup) in a legacy system handling data that directly impacted people’s day-to-day stability. Working outside the Salesforce ecosystem, especially in a system nearing end-of-life, taught me things platform-only experience never could. The stakes were real This was data tied to people’s housing, eligibility, and stability. Because I was running update and delete queries directly against the database, there was no safety net. Here’s what that experience taught me: Data exists beyond the platform • SOQL works within Salesforce’s specific structure, but SQL forces you to understand what’s underneath: tables, joins, and constraints. Once you see data at that level, your perspective shifts. Salesforce isn’t the data; it’s the interface to it. Data quality is not automatic • When you are working directly with live data, you see everything: inconsistent records, missing relationships, and edge cases the system doesn’t handle well. You do not assume the data is clean. You verify it. That mindset carries over into every Salesforce environment I work in today. Salesforce doesn’t live in isolation • Every org connects to something: financial systems, legacy platforms, or external databases. If you can only operate inside Salesforce, you will always rely on someone else to fill the gaps. Understanding data across systems makes you far more effective when dealing with integrations or reconciliation issues. Data accuracy is a responsibility, not a metric • When data affects whether someone has stable housing or access to support, accuracy is not a stretch goal. It is the baseline. That standard changed how I approach data in every environment since. I don’t usually lead with SQL as a skill. Most of my hands-on experience came from that earlier work, but it shaped how I approach every data problem I encounter today. Have you worked with data outside of Salesforce? How did it change the way you think about data inside the platform? #Salesforce #SQL #SOQL #DataQuality #SalesforceAdmin

Your Apex works perfectly for 1 record. Then a data loader imports 10,000 and everything breaks. This is the most common Salesforce development failure. And it is 100% preventable. The rule is simple: never put SOQL inside a for loop. Never put DML inside a for loop. Query once before the loop, build a Map, look up in O(1) time inside the loop. One DML statement handles 10,000 records as easily as it handles 1. Full guide: all 10 governor limits with monitoring code, every Map pattern you need, SOQL index reference + optimization (selectivity, AggregateResult, LIMIT), complete trigger framework (thin trigger + handler class + bypass switch + recursion guard), Batch Apex with Database.Stateful, and 6 dark corners that crash production orgs. The trigger framework alone will change how you write Apex forever. 📎 Free PDF. Developer Edition org — zero cost. #Salesforce #Apex #SalesforceDev #SalesforceOhana #Trailhead #SOQL #ApexDevelopment #Bulkification #GovernorLimits

🚀 Batch Apex: Iterator vs QueryLocator : When to Use What? While working with Batch Apex, one common confusion is choosing between Iterator and Database.QueryLocator in the start() method. This is also one of the important decisions to keep your batch running smoothly. Here's a simple breakdown : 🔹 1. Database.QueryLocator • Designed for large data volumes (up to 50 million records) • Uses SOQL query directly • Salesforce handles chunking internally using cursor • More efficient for simple, bulk data processing ✅ Best when: 𝗬𝗼𝘂’𝗿𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗿𝗲𝗰𝗼𝗿𝗱𝘀 𝗳𝗿𝗼𝗺 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗾𝘂𝗲𝗿𝘆 𝗡𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗽𝗿𝗲-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗶𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗥𝗲𝗱𝘂𝗰𝗲𝘀 𝘁𝗵𝗲 𝗖𝗣𝗨 𝗮𝗻𝗱 𝗵𝗲𝗮𝗽 𝘂𝘀𝗮𝗴𝗲 𝗧𝗵𝗶𝘀 𝘂𝘁𝗶𝗹𝗶𝘇𝗲𝘀 𝘁𝗵𝗲 𝗯𝗮𝘁𝗰𝗵 𝗮𝗽𝗲𝘅'𝘀 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝘁𝗼 𝗶𝘁𝘀 𝗳𝘂𝗹𝗹𝗲𝘀𝘁 🔹 2. Iterable • Gives full control over the dataset • Can include custom logic, multiple sources, API data • Limited by standard governor limits (no 50M support) ✅ Best when: 𝗗𝗮𝘁𝗮 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗹𝗼𝗴𝗶𝗰 𝗣𝗿𝗲-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝗱 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝗮𝘁𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 💡 On the Whole: 👉 Use QueryLocator for performance & scale 👉 Use Iterable for flexibility & complex logic This is a frequently used concept ,not complex, but clarity matters a lot! #Salesforce #Apex #BatchApex #SalesforceDeveloper #TechLearning