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Millisecond Latency in Databricks Apps with Lakebase",{"rendered":526,"protected":527},"\n\u003Cp>Databricks has been steadily expanding its tech stack to include more and more technologies. While focusing primarily on their Lakehouse features at the beginning, they quickly became a one-stop shop for basically anything to do with data and machine learning (ML). \u003C\u002Fp>\n\n\n\n\u003Cp>With their Databricks Apps feature – released in the second half of 2024 – Databricks gave their users another possibility to leverage data and models, by providing an easy way of creating dashboards and data apps completely on Databricks. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-tl-dr-nbsp\">TL;DR \u003C\u002Fh2>\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Lakebase consistently outperforms SQL Warehouse in low-latency operations, making it ideal for real-time applications. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>The relative speedup is most pronounced for small batch sizes, while large-scale bulk operations see diminishing returns. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>SQL Warehouse may still be suitable for scenarios where latency is less critical or where specific features outweigh performance considerations. \u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-solving-real-time-data-app-performance-challenges-nbsp\">Solving Real-Time Data App Performance Challenges \u003C\u002Fh2>\n\n\n\u003Cp>Building real-time interactive data apps has traditionally faced a significant shortcoming if performing reads and writes on the data sources frequently. Using the SQL Warehouse to carry out these operations, the latency could be in the order of seconds. Waiting several seconds after entering data in a real-time app can severely impact user experience. One solution would have been to circumvent the SQL warehouse for these updates and make use of a separate database service like a Postgres instance and, consequently, configure resources, connections and authorization on top.  \u003C\u002Fp>\n\n\n\n\u003Cp>Databricks acknowledged this limitation and so in June 2025 they \u003Ca href=\"https:\u002F\u002Fwww.databricks.com\u002Fblog\u002Fannouncing-lakebase-public-preview\" target=\"_blank\" rel=\"noreferrer noopener\">introduced their new Lakebase feature\u003C\u002Fa> – a Postgres database instance directly built into Databricks. With Lakebase Databricks commits to alleviating all the previously discussed problems, promising to cut down the latency to just a couple of milliseconds. \u003C\u002Fp>\n\n\n\n\u003Cp>Let’s take a close look at Lakebase, focusing primarily on its performance to determine if Databricks’ claims are realistic and what to expect when switching from the SQL warehouse to the new Lakebase. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-our-benchmark-approach-nbsp\">Our Benchmark Approach \u003C\u002Fh2>\n\n\n\u003Cp>We were particularly interested in the combination of Lakebase and a real-time application. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-test-setup-nbsp\">Test setup \u003C\u002Fh3>\n\n\n\u003Cp>Our team created a new Databricks App to account for any communication overheads that might not be present when testing the performance through other means and, therefore, have a realistic setting. By creating a very basic to-do Databricks App and realistic data with varied types, we ensure to be as close as possible to a practicable application. Each to-do item includes text content, a Boolean indicator whether it is already completed, and a timestamp that signifies the task’s creation time.  \u003C\u002Fp>\n\n\n\n\u003Cp>Thus, a typical line of data that we insert into, modify and read from our respective databases looks like this: \u003C\u002Fp>\n\n\n\n\u003Cpre style=\"background:#1e1e1e;color:#d4d4d4;font-family:Consolas,Monaco,'Courier New',monospace;font-size:13px;line-height:1.5;padding:16px;border-radius:6px;overflow-x:auto;white-space:pre;\">\n(1, 'todo: benchmark_task_0001', false, TIMESTAMP '2025-09-04 12:34:56')\n\u003C\u002Fpre>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-benchmark-configuration-nbsp\">Benchmark configuration \u003C\u002Fh3>\n\n\n\u003Cp>Our benchmark aims to test the basic functionality of both connection modes, so we chose to time read, update, and insert operations. Additionally, we explored different data sizes for insertion to examine the scaling behavior of both techniques. \u003C\u002Fp>\n\n\n\n\u003Cp>For this purpose, we created two new tables. One in our catalog connected to our Lakebase instance, the other one as a simple Delta Table in Unity Catalog. To ensure comparability between both connection methods we chose the smallest instance for both resources, i.e. a size of \u003Cstrong>one Capacity Unit\u003C\u002Fstrong> (CU) for the Lakebase Postgres instance and a \u003Cstrong>2X-Small\u003C\u002Fstrong> SQL warehouse of type Pro. We used the ConnectionPool implementation of psycopg2 to connect to the Lakebase and the Databricks SQL-connector to establish a connection to the SQL Warehouse. \u003C\u002Fp>\n\n\n\n\u003Cp>We started each suite run with an empty database and \u003Cstrong>populated it with 10,000 entries\u003C\u002Fstrong> to simulate a real-world scenario that would of course not be empty. Thus, each database had the same size as one benchmark run to another. Moreover, before each operation sequence, we performed \u003Cstrong>three warm-up operations\u003C\u002Fstrong> of the same type to make sure the system was in a steady state and that possible initialization costs did not distort the measured performance.  \u003C\u002Fp>\n\n\n\n\u003Cp>Lastly, we repeated every operation \u003Cstrong>25 times\u003C\u002Fstrong> to ensure statistical significance and reduce the impact of outliers, giving us a clearer picture of performance trends across both systems. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-testing-methodology-nbsp\">Testing Methodology \u003C\u002Fh2>\n\n\n\u003Cp>To ensure a fair and consistent comparison between \u003Cstrong>Databricks Lakebase\u003C\u002Fstrong> and \u003Cstrong>SQL Warehouse\u003C\u002Fstrong>, we designed a benchmark suite that evaluates the performance of common database operations: \u003Cstrong>select\u003C\u002Fstrong>, \u003Cstrong>update\u003C\u002Fstrong>, and \u003Cstrong>insert\u003C\u002Fstrong>. Each benchmark executes representative SQL statements repeatedly over a controlled dataset, allowing us to observe latency, throughput, and system behavior under load. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-select-benchmark-measuring-read-performance-nbsp\">SELECT benchmark: Measuring read performance \u003C\u002Fh3>\n\n\n\u003Cp>The \u003Cem>select\u003C\u002Fem> operation in our benchmark looks like this: \u003C\u002Fp>\n\n\n\n\u003Cpre style=\"background:#1e1e1e;color:#d4d4d4;font-family:Consolas,Monaco,'Courier New',monospace;font-size:13px;line-height:1.5;padding:16px;border-radius:6px;overflow-x:auto;white-space:pre;\">\nSELECT id, task, completed, created_at FROM {table} WHERE id = %s\n\u003C\u002Fpre>\n\n\n\n\u003Cp>This query simulates a user retrieving a single to-do by ID. We made sure to always query a different ID for each iteration to avoid any caching to take place that might skew the performance in any way. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-update-benchmark-testing-performance-of-in-place-data-changes-nbsp\">UPDATE benchmark: Testing performance of in-place data changes \u003C\u002Fh3>\n\n\n\u003Cp>We measured update performance like this: \u003C\u002Fp>\n\n\n\n\u003Cpre style=\"background:#1e1e1e;color:#d4d4d4;font-family:Consolas,Monaco,'Courier New',monospace;font-size:13px;line-height:1.5;padding:16px;border-radius:6px;overflow-x:auto;white-space:pre;\">\nUPDATE {table} SET completed = NOT completed WHERE id = %s\n\u003C\u002Fpre>\n\n\n\n\u003Cp>This update flips the completed status of a single task. It mimics a transactional scenario where users frequently toggle task status (e.g., marking items done\u002Fundone in a task tracker). Similar to the SELECT benchmark, we query a different ID in each iteration to avoid inadvertently measuring caching effects. For the Delta Table in Unity Catalog deletion vectors have been enabled. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-insert-benchmark-evaluating-write-performance-nbsp\">INSERT benchmark: Evaluating write performance \u003C\u002Fh3>\n\n\n\u003Cp>This benchmark measures write performance by inserting new tasks into the table using: \u003C\u002Fp>\n\n\n\n\u003Cpre style=\"background:#1e1e1e;color:#d4d4d4;font-family:Consolas,Monaco,'Courier New',monospace;font-size:13px;line-height:1.5;padding:16px;border-radius:6px;overflow-x:auto;white-space:pre;\">\nINSERT INTO {table} (task, completed, created_at) VALUES (%s, %s, %s)\n\u003C\u002Fpre>\n\n\n\n\u003Cp>One subtle but important difference between the systems lies in how primary keys are handled. Postgres can auto-generate the id field, whereas SQL Warehouse (via Databricks) requires explicit ID assignment. To ensure a fair comparison, we did not use Lakebase’s automatic ID generation and manually assigned IDs in both systems, incrementing by one for each inserted row. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-bulk-insert-testing-realistic-ingestion-patterns-nbsp\">Bulk INSERT testing: Realistic ingestion patterns \u003C\u002Fh3>\n\n\n\u003Cp>To assess how each system handles higher ingestion volumes and scales under load, we included a bulk insert benchmark. This test is critical for understanding throughput characteristics, especially in scenarios where large datasets are ingested in batches — such as ETL pipelines or periodic sync jobs. \u003C\u002Fp>\n\n\n\n\u003Cp>We constructed multi-row insert statements like: \u003C\u002Fp>\n\n\n\n\u003Cpre style=\"background:#1e1e1e;color:#d4d4d4;font-family:Consolas,Monaco,'Courier New',monospace;font-size:13px;line-height:1.5;padding:16px;border-radius:6px;overflow-x:auto;white-space:pre;\">\nINSERT INTO {table} (id, task, completed, created_at)  \n\nVALUES (val1), (val2), ..., (valn)\n\u003C\u002Fpre>\n\n\n\n\u003Cp>For SQL Warehouse, the Python client (databricks.sql) currently does not support executemany. Instead, we batched inserts by manually constructing a single SQL statement with multiple value tuples. Although Postgres supports executemany natively, we opted for the same manual batching approach to ensure 1-to-1 execution logic across both systems. This decision ensured consistent client behavior and allowed us to isolate the performance impact of bulk ingestion more accurately. \u003C\u002Fp>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-performance-comparison-results-databricks-lakebase-vs-sql-warehouse-nbsp\">Performance Comparison Results: Databricks Lakebase vs. SQL Warehouse \u003C\u002Fh2>\n\n\n\u003Cp>Our benchmarking results highlight the stark performance differences between Lakebase and SQL Warehouse across various database operations. Each operation was repeated ten times, with three warm-up iterations to ensure accuracy. \u003C\u002Fp>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-single-operation-latency-milliseconds-vs-seconds-nbsp\">Single operation latency: Milliseconds vs. seconds \u003C\u002Fh3>\n\n\n\u003Cp>All operations in Lakebase demonstrate remarkably low median latencies, ranging from \u003Cstrong>14 ms to 16 ms\u003C\u002Fstrong>, with minimum latencies in the single digits and maximum latencies in the 25 ms range (see \u003Cstrong>Figure 1\u003C\u002Fstrong>). This consistency underscores Lakebase’s efficiency for real-time applications. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"675\" height=\"690\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-25.png\" alt=\"Figure 1: Operation latency for select, write and update statements, repeated for 25 iterations using Lakebase.\" class=\"wp-image-11140\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-25.png 675w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-25-98x100.png 98w\" sizes=\"auto, (max-width: 675px) 100vw, 675px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Figure 1: Operation latency for select, write and update statements, repeated for 25 iterations using Lakebase.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\u003Cp>In contrast, SQL Warehouse exhibits significantly higher and more variable latencies. \u003Cstrong>SELECT\u003C\u002Fstrong> operations average around \u003Cstrong>700 ms\u003C\u002Fstrong>, while \u003Cstrong>UPDATE\u003C\u002Fstrong> operations can take up to \u003Cstrong>4.5 seconds\u003C\u002Fstrong> (see \u003Cstrong>Figure 2\u003C\u002Fstrong>). While a 700 ms delay might be tolerable for initial data retrieval in some real-time applications, multi-second wait times for updates are likely unacceptable in most scenarios.\u003Cstrong> \u003C\u002Fstrong> \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"702\" height=\"699\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-23.png\" alt=\"Figure 2 Operation latency for select, write and update statements, repeated for 25 iterations using the SQL Warehouse.\" class=\"wp-image-11126\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-23.png 702w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-23-100x100.png 100w\" sizes=\"auto, (max-width: 702px) 100vw, 702px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Figure 2: Operation latency for select, write and update statements, repeated for 25 iterations using the SQL Warehouse.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-relative-speedup-up-to-160x-performance-improvement-nbsp\">Relative Speedup: Up to 160x performance improvement \u003C\u002Fh3>\n\n\n\u003Cp>Lakebase’s performance advantage is further emphasized when comparing relative speedups. As shown in \u003Cstrong>Figure 3\u003C\u002Fstrong>, Lakebase achieves up to \u003Cstrong>160× faster\u003C\u002Fstrong> operations than SQL Warehouse, with the most significant gains observed in \u003Cstrong>UPDATE\u003C\u002Fstrong> operations. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"807\" height=\"455\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-26.png\" alt=\"Figure 3. Median speedup (latency) of Lakebase operations compared to the SQL Warehouse for 25 iterations.\" class=\"wp-image-11141\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-26.png 807w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-26-800x451.png 800w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-26-100x56.png 100w\" sizes=\"auto, (max-width: 807px) 100vw, 807px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Figure 3: Median speedup (latency) of Lakebase operations compared to the SQL Warehouse for 25 iterations.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"h-bulk-insert-performance-scalability-aspect\">Bulk INSERT performance: Scalability aspect\u003C\u002Fh3>\n\n\n\u003Cp>We also evaluated the relative speedup of \u003Cstrong>INSERT\u003C\u002Fstrong> operations for varying batch sizes. For small batches (1–100 rows), Lakebase delivers a speedup of \u003Cstrong>100–120×\u003C\u002Fstrong> over SQL Warehouse. However, as batch size increases, the relative performance gain diminishes sharply: dropping to \u003Cstrong>60×\u003C\u002Fstrong> at 1,000 rows, \u003Cstrong>18×\u003C\u002Fstrong> at 10,000 rows, and stabilizing around \u003Cstrong>4.5×\u003C\u002Fstrong> for very large batches (100,000–200,000 rows) (see \u003Cstrong>Figure 4\u003C\u002Fstrong>).  \u003C\u002Fp>\n\n\n\n\u003Cp>This trend suggests that while Lakebase excels in low-latency, small-batch operations, its advantage narrows for large-scale bulk inserts. Additionally, we encountered limitations with the Databricks SQL connector, which failed to execute \u003Cstrong>INSERT statements with more than 200,000\u003C\u002Fstrong> rows. However, a scenario where the insertion of several hundreds of thousands of lines via a real-time data application is needed is probably not very common and such workloads are better suited for the use of different strategies specifically optimized for this, like spark-compute-clusters. \u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"881\" height=\"448\" src=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-27.png\" alt=\"Figure 4. Median speedup (latency) of Lakebase over SQL Warehouse for Bulk-INSERT operations for different number of inserted lines. Each insert operation has been repeated 25 times with an initial three warm-up iterations before.\" class=\"wp-image-11142\" srcset=\"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-27.png 881w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-27-800x407.png 800w, https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fimage-27-100x51.png 100w\" sizes=\"auto, (max-width: 881px) 100vw, 881px\" \u002F>\u003Cfigcaption class=\"wp-element-caption\">Figure 4: Median speedup (latency) of Lakebase over SQL Warehouse for Bulk-INSERT operations for different number of inserted lines. Each insert operation has been repeated 25 times with an initial three warm-up iterations before.\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"h-the-bottom-line-nbsp-nbsp\">The Bottom Line  \u003C\u002Fh2>\n\n\n\u003Cp>Databricks has long been the go-to \u003Ca href=\"https:\u002F\u002Fstaging.cbtw.tech\u002Fservice-overview\u002Fai-data-platforms\u002F\" target=\"_blank\" rel=\"noreferrer noopener\">platform for data\u003C\u002Fa> and ML, but real-time interactivity remained a challenge—until \u003Cstrong>Lakebase\u003C\u002Fstrong>. Previously, SQL Warehouse latencies in the seconds made responsive apps impractical, forcing users to rely on external databases. \u003C\u002Fp>\n\n\n\n\u003Cp>Our benchmarks confirm Lakebase’s promise: \u003Cstrong>millisecond-level latencies\u003C\u002Fstrong> and up to \u003Cstrong>160× faster \u003C\u002Fstrong>processing times than SQL Warehouse. It removes the need for external databases or workaround solutions, enabling truly real-time data apps—natively on Databricks. \u003C\u002Fp>\n\n\n\n\u003Cp>\u003C\u002Fp>\n",false,{"rendered":529,"protected":527},"\u003Cp>Real-time Databricks apps can feel snappy—until your first write hits a seconds-long wall. Lakebase, Databricks’ built-in Postgres (June 2025), claims millisecond responses. We put it to the test with a working Databricks App and a controlled benchmark across SELECT, UPDATE, INSERT, and bulk ingestion. Dive in for the full setup, queries, charts, and a clear decision guide on when to choose Lakebase vs. SQL Warehouse.\u003C\u002Fp>\n",43,11144,"open","standard",{"_acf_changed":527,"content-type":8,"inline_featured_image":527,"footnotes":8},[536],46,[],[],[540],129,[542,521,543,544,545,546,547,548,549],"post-11111","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","cbtw_services-ai-data-platforms",{"authors":8,"article_authors":551,"direct_external_link":8,"post_region":555,"layout_builder":556},[552],{"author_name":553,"author_url":554},"Jonas S.","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjonas-stadtm%C3%BCller-b2a790230\u002F","global",[557],{"acf_fc_layout":558,"title":195,"ui_v2":559,"short_description":560,"cbtw_related_group_filters":561,"items":527,"link":566,"hubspot_form_id":8,"mainPostId":516,"region":555},"block_insights",true,"Access related expert insights",{"cbtw_related_category_filter":562,"cbtw_related_tag_filter":563,"cbtw_related_industry_filter":564,"cbtw_related_service_filter":565},[197,221],[],[],[],{"title":567,"url":568,"target":569},"View all insights","https:\u002F\u002Fstaging.cbtw.tech\u002Finsights","_self","\u003C!-- This site is optimized with the Yoast SEO Premium plugin v25.4 (Yoast SEO v25.4) - https:\u002F\u002Fyoast.com\u002Fwordpress\u002Fplugins\u002Fseo\u002F -->\n\u003Ctitle>Achieving Millisecond Latency in Databricks Apps with Lakebase - CBTW\u003C\u002Ftitle>\n\u003Cmeta name=\"description\" content=\"Explore how Databricks’ Lakebase stacks up against SQL Warehouse in real-time app performance. 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The Challenge  A single RfP can include dozens of documents: specifications, drawings, forms and clarifications, all referencing each other, easily exceeding 100 pages. The information you need is scattered, and no two tenders are […]","2026-06-03","https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Fgenai-rfp-processing-a-case-study-with-human-in-the-loop","https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fgenai-rfp-processing-human-in-the-loop-1.jpg",{"medium":972,"large":973,"full":970},"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fgenai-rfp-processing-human-in-the-loop-1-640x800.jpg","https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002Fgenai-rfp-processing-human-in-the-loop-1-819x1024.jpg",{"id":975,"title":976,"description":8,"excerpt":977,"date":979,"categories":210,"link":980,"image":981,"images":982},15073,"Fintech App Development in Singapore for Banks and Payment Providers",{"rendered":978},"Singapore is one of the most demanding environments in the world to build financial software. The market is mature, institution-led, and unforgiving of poor digital experiences, especially when products serve banks, payment networks, fintech platforms, and financial services firms.  This is the context in which CBTW builds fintech apps in Singapore. Mobile app development in Singapore for […]","2026-06-02","https:\u002F\u002Fstaging.cbtw.tech\u002Finsights\u002Ffintech-app-development-in-singapore","https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FThiet-ke-chua-co-ten-2.webp",{"medium":983,"large":984,"full":981},"https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FThiet-ke-chua-co-ten-2-800x533.webp","https:\u002F\u002Fstaging-admin.cbtw.tech\u002Fwp-content\u002Fuploads\u002FThiet-ke-chua-co-ten-2-1024x683.webp",1786591992071]