Results for “javax”

Process and analyze large tabular datasets (billions of rows) that exceed available RAM using lazy, out-of-core DataFrames with fast aggregations, visualization, and machine learning integration.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Wydruki i raporty w Soneta (enova365, Triva) — pliki .repx (DevExpress XtraReports, serializowany XML). Sięgaj przy KAŻDYM zadaniu z wydrukiem/raportem: faktura, dokument, deklaracja, lista, zestawienie; utworzenie, przeróbka lub poprawa .repx (też z załącznika); pytanie „jak w wydruku zrobić X”. Typowe X: podpięcie danych (lista, dokument z pozycjami, master-detail, podraport), podział stron, kolumna Lp, tabele, grupowanie i sumy, formatowanie warunkowe, kwoty, nakładanie wartości na skan formularza, nagłówek/stopka/podpisy, arkusz stylów (.repss), rejestracja wydruku. Wyzwalają też pojęcia: XtraReportsLayoutSerializer, StyleSheetSerializer, pasma (DetailBand/DetailReportBand/GroupHeaderBand/SubBand), kontrolki (XRLabel/XRTable/AmountLabel), BusinessDataSource/DataKind, ReportContext, ExpressionBindings/Summary/CalculatedFields/FilterString, [assembly: DxReport], ReportSnippet/[DxBind]. Wystarczy „wydruk”, „raport” lub „.repx” w kontekście Soneta. NIE dla: business.xml, form.xml, SQL, Excel, Word .dotx.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Process and analyze tabular datasets larger than RAM using lazy, out-of-core DataFrames, with fast aggregations, visualization, and machine learning integration.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.

Use essa skill para processar e analisar grandes conjuntos de dados tabulares (bilhões de linhas) que excedem a RAM disponível. Vaex excels em operações DataFrame out-of-core, avaliação lazy, agregações rápidas, visualização eficiente de big data e machine learning em datasets grandes. Aplique quando usuários precisarem trabalhar com arquivos CSV/HDF5/Arrow/Parquet grandes, realizar estatísticas rápidas em datasets massivos, criar visualizações de big data ou construir pipelines de ML que não cabem em memória.

Build reactive backends with Convex: schema design, TypeScript functions, real-time subscriptions, auth, file storage, scheduling, and deployment.

Design schemas, write TypeScript functions, set up real-time subscriptions, authentication, file storage, scheduling, and deployment for Convex reactive backend projects.

Build and manage Convex backends: schema design, TypeScript functions, real-time subscriptions, auth, file storage, scheduling, and deployment.

Design schemas, write TypeScript functions, and manage real-time subscriptions, auth, file storage, scheduling, and deployment for Convex backends.