Alternatives to Tabular

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

DbVisualizer is one of the world’s most popular database clients. Developers, analysts, and DBAs use it to advance their SQL experience with modern tools to visualize and manage their databases, schemas, objects, and table data and to auto-generate, write and optimize queries. It has extended support for 30+ of the major databases and has basic-level support for all databases that can be accessed with a JDBC driver. DbVisualizer runs on all major OSes. Free and Pro versions are available.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.

VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale. In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries. Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes. Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration. Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.

Processing and storage of tabular datasets, e.g. CSV or Parquet files. Large result set transfer to client. Large client/server installations are required for central enterprise data warehousing. Multiple concurrent processes can be used to write to a single database. DuckDB is a relational database management software (RDBMS). It is a system to manage data stored in relational databases. A relation is basically a mathematical term for a particular table. Each table is a named collection. Each row in a table has the same number of named columns. Each column is of a particular data type. Schemas are used to store tables, and a collection can be accessed to access the entire database.

StarRocks offers at least 300% more performance than other popular solutions, whether you're using a single or multiple tables. With a rich set connectors, you can ingest real-time data into StarRocks for the latest insights. A query engine that adapts your use cases. StarRocks allows you to scale your analytics easily without moving your data or rewriting SQL. StarRocks allows a rapid journey between data and insight. StarRocks is unmatched in performance and offers a unified OLAP system that covers the most common data analytics scenarios. StarRocks offers at least 300% faster performance than other popular solutions, whether you are working with one table or many. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

QuasarDB is Quasar's brain. It is a high-performance distributed, column-oriented, timeseries database management software system that delivers real-time data for petascale use cases. You can save up to 20X on your disk usage Quasardb compression and ingestion are unmatched. Feature extraction can be performed up to 10,000 times faster. QuasarDB is able to extract features from raw data in real-time thanks to a combination of a builtin map/reduce engine, an aggregate engine that leverages SIMD from modern processors, and stochastic indices that consume virtually no disk space.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

Presto is an open-source distributed SQL query engine that allows interactive analytic queries against any data source, from gigabytes up to petabytes.

Palo helps enterprises create the PB level MPP architecture data warehouse services in just a few minutes and import massive data from RDS BOS and BMR. Palo is able to perform multi-dimensional analysis of big data. Palo is compatible to mainstream BI tools. Data analysts can quickly gain insights by analyzing and displaying the data visually. It has an industry-leading MPP engine with column storage, intelligent indexes, and vector execution functions. It can also provide advanced analytics, window functions and in-library analytics. You can create a materialized table and change its structure without suspending service. It supports flexible data recovery.

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

Amazon Athena allows you to easily analyze data in Amazon S3 with standard SQL. Athena is serverless so there is no infrastructure to maintain and you only pay for the queries you run. Athena is simple to use. Simply point to your data in Amazon S3 and define the schema. Then, you can query standard SQL. Most results are delivered in a matter of seconds. Athena makes it easy to prepare your data for analysis without the need for complicated ETL jobs. Anyone with SQL skills can quickly analyze large-scale data sets. Athena integrates with AWS Glue Data Catalog out-of-the box. This allows you to create a unified metadata repositorie across multiple services, crawl data sources and discover schemas. You can also populate your Catalog by adding new and modified partition and table definitions. Schema versioning is possible.

Now that your data has been in motion, it is time to make sense. Stream processing allows you to extract instant insights from your data streams but it can be difficult to set up the infrastructure. Confluent created ksqlDB to support stream processing applications. Continuously processing streams of data from your business will make your data actionable. The intuitive syntax of ksqlDB allows you to quickly access and augment Kafka data, allowing development teams to create innovative customer experiences and meet data-driven operational requirements. ksqlDB is a single solution that allows you to collect streams of data, enrich them and then serve queries on new derived streams or tables. This means that there is less infrastructure to manage, scale, secure, and deploy. You can now focus on the important things -- innovation -- with fewer moving parts in your data architecture.

You can create flat or relational databases with unlimited fields, tables, and rows. A custom report builder is included. Create custom reports by connecting to compatible ODBC databases. You can create your own databases. Here are some highlights: Filter tables instantly - Ultra simple graphical-user-interface - One-click table and data form creation - You can open up to 5 databases simultaneously Export your data to comma-separated files - Create custom reports to all your databases - A complete helpfile for creating database reports - You can print tables and queries directly from your data grid - Supports any SQL standard your ODBC compatible databases require For best performance and user experience, please install and run this database app with full administrator rights. Requirements: . 1024x768 Display Size . Windows 98 / XP / Windows 8 / Windows 10 - 32bit or 64bit No Java or DotNet are required. Green Energy Software. One step at a time, saving the planet

Perhaps you want to allow your clients to access their LIMS data via the internet. AIS labPortal makes it possible to do exactly that. Sample analyses can be emailed to customers, but not as paper copies. Clients can access their data using their unique login and security code from their computer. This is safer, faster, and more environmentally friendly than sending paper copies of sample analyses to customers. labPortal, a web-based portal, securely stores client's sample information and data in a cloud. Clients can access this data instantly from any computer, tablet, or smartphone. LabPortal's interface is an 'inbox' design that is simple and easy to use. It features an enhanced query engine, conditional highlight and Microsoft Excel export. The software includes an easy-to-use sample registration tool that allows users to preregister samples online. Transcribing data can be tedious and time-consuming.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Schema-free SQL query engine for Hadoop, NoSQL, and Cloud Storage

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

Rich interactive visualizations to identify patterns in user and company flows with full visibility of computation. In less than 10 lines of code, a small set of sequence operators can provide full expressivity and finely-grained control. A query engine that allows you to trade between query speed, precision and cost according your needs. Motif currently uses a custom-built DSL, called Sequence Operations Language. We believe it is more natural than SQL and more powerful that a drag-and drop interface. We built a custom algorithm to optimize sequence queries. We also trade off precision for query speed, which is not used in decision-making.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.

Scale from 0 to millions of events every second. Arroyo is shipped as a single compact binary. Run locally on MacOS, Linux or Kubernetes for development and deploy to production using Docker or Kubernetes. Arroyo is an entirely new stream processing engine that was built from the ground-up to make real time easier than batch. Arroyo has been designed so that anyone with SQL knowledge can build reliable, efficient and correct streaming pipelines. Data scientists and engineers are able to build real-time dashboards, models, and applications from end-to-end without the need for a separate streaming expert team. SQL allows you to transform, filter, aggregate and join data streams with results that are sub-second. Your streaming pipelines should not page someone because Kubernetes rescheduled your pods. Arroyo can run in a modern, elastic cloud environment, from simple container runtimes such as Fargate, to large, distributed deployments using the Kubernetes logo.

Don't let game, app, or device crashes stop you from having a great experience. Backtrace automates cross-platform exception management and cross-platform crash management so that you can focus on shipping. Cross-platform callstack, event aggregation, and monitoring. A single system can process errors from panics and core dumps, minidumps, as well as during runtime across your stack. Backtrace generates searchable, structured error reports from your data. Automated analysis reduces time to resolution by surfacing important signals which lead engineers to the crash root cause. Rich integrations into dashboards and notification systems mean that you don't have to worry about missing a detail. Backtrace's rich queries engine will help you answer the questions that are most important to you. A high-level overview of errors, prioritization and trends across all projects can be viewed. You can search through key data points as well as your own custom data for all errors.

Your cloud data platform. Access to any data you need with unlimited scalability. All your data is available to you, with the near-infinite performance and concurrency required by your organization. You can seamlessly share and consume shared data across your organization to collaborate and solve your most difficult business problems. You can increase productivity and reduce time to value by collaborating with data professionals to quickly deliver integrated data solutions from any location in your organization. Our technology partners and system integrators can help you deploy Snowflake to your success, no matter if you are moving data into Snowflake.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

SPListX for SharePoint allows you to export picture library contents, metadata, list items and associated file attachments to Windows File System. SharePoint site, libraries and folders can be exported to Windows File System. SPListX supports SharePoint 2019 / SharePoint 2016, SharePoint 2013 / SharePoint 2010, SharePoint 2007 / SharePoint 2003 & Office 365.

BigLake is a storage platform that unifies data warehouses, lakes and allows BigQuery and open-source frameworks such as Spark to access data with fine-grained control. BigLake offers accelerated query performance across multicloud storage and open formats like Apache Iceberg. You can store one copy of your data across all data warehouses and lakes. Multi-cloud governance and fine-grained access control for distributed data. Integration with open-source analytics tools, and open data formats is seamless. You can unlock analytics on distributed data no matter where it is stored. While choosing the best open-source or cloud-native analytics tools over a single copy, you can also access analytics on distributed data. Fine-grained access control for open source engines such as Apache Spark, Presto and Trino and open formats like Parquet. BigQuery supports performant queries on data lakes. Integrates with Dataplex for management at scale, including logical organization.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Timeplus is an easy-to-use, powerful and cost-effective platform for stream processing. All in one binary, easily deployable anywhere. We help data teams in organizations of any size and industry process streaming data and historical data quickly, intuitively and efficiently. Lightweight, one binary, no dependencies. Streaming analytics and historical functionality from end-to-end. 1/10 of the cost of comparable open source frameworks Transform real-time data from the market and transactions into real-time insight. Monitor financial data using append-only streams or key-value streams. Implement real-time feature pipelines using Timeplus. All infrastructure logs, metrics and traces are consolidated on one platform. In Timeplus we support a variety of data sources through our web console UI. You can also push data using REST API or create external streams, without copying data to Timeplus.

Permify is a service that helps developers create and manage scalable, fine-grained access control systems in their applications. Permify, which is based on Google's Zanzibar authorization service, allows for the structuring of authorizations models, storage of authorizations in preferred databases and interaction with its API. It supports multiple access models, such as Role-Based Access Control and Attribute-Based Access Control. This allows for the creation and management of granular policies and permissions. Permit centralized authorization logic by abstracting it from codebases to make reasoning, testing and debugging easier. It provides flexible storage options for policies and a role manager that can handle RBAC hierarchy roles. The platform supports filtered policies for efficient enforcement of policies in large, multitenant environments.

Iceberg is an efficient format for large analytical tables. Iceberg brings the simplicity and reliability of SQL tables to the world of big data. It also allows engines like Spark, Trino Flink Presto Hive Impala and Impala to work safely with the same tables at the same time. Iceberg supports SQL commands that are flexible to merge new data, update rows, and perform targeted deletions. Iceberg can eagerly write data files to improve read performance or it can use delete-deltas for faster updates. Iceberg automates the tedious, error-prone process of generating partition values for each row in a table. It also skips unnecessary files and partitions. There are no extra filters needed for fast queries and the table layout is easily updated when data or queries change.

Data analysts can now gain insights faster and without any barriers. Intelligent data stack automation, gain insights from your data 10x quicker with AI. Intelligence for the modern data-stack has arrived, a technology that was developed at Stanford's AI lab and is now available to enterprises. Use natural language to extract value from your messy data, complex and siloed in minutes. Tell your data what you want and it will generate code to execute. Automate complex data tasks in a way that is specific to your company and not covered by templated solutions. Automate data-intensive workflows using the modern data stack. Discover insights in minutes and not months. Uniquely designed and tuned to your organization's requirements. Snowflake, Databricks Redshift, BigQuery and more are integrated with dbt.

Hackolade is the pioneer for data modeling of NoSQL and multi-model databases, providing a comprehensive suite of data modeling tools for various NoSQL databases and APIs. Hackolade is the only data modeling tool for MongoDB, Neo4j, Cassandra, ArangoDB, BigQuery, Couchbase, Cosmos DB, Databricks, DocumentDB, DynamoDB, Elasticsearch, EventBridge Schema Registry, Glue Data Catalog, HBase, Hive, Firebase/Firestore, JanusGraph, MariaDB, MarkLogic, MySQL, Oracle, PostgreSQL, Redshift, ScyllaDB, Snowflake, SQL Server, Synapse, TinkerPop, YugabyteDB, etc. It also applies its visual design to Avro, JSON Schema, Parquet, Protobuf, Swagger and OpenAPI, and is rapidly adding new targets for its physical data modeling engine. The software is user-friendly and simple to use yet provides powerful visuals and graphic data modeling to smooth the onboarding of NoSQL technology. Its software tools help functional analysts, designers, architects, and DBAs involved with NoSQL technology achieve greater transparency and control, resulting in reduced development time, increased application quality, and lower execution risks across the enterprise.

CData Sync is a universal database pipeline that automates continuous replication between hundreds SaaS applications & cloud-based data sources. It also supports any major data warehouse or database, whether it's on-premise or cloud. Replicate data from hundreds cloud data sources to popular databases destinations such as SQL Server and Redshift, S3, Snowflake and BigQuery. It is simple to set up replication: log in, select the data tables you wish to replicate, then select a replication period. It's done. CData Sync extracts data iteratively. It has minimal impact on operational systems. CData Sync only queries and updates data that has been updated or added since the last update. CData Sync allows for maximum flexibility in partial and full replication scenarios. It ensures that critical data is safely stored in your database of choice. Get a 30-day trial of the Sync app for free or request more information at www.cdata.com/sync

Interactive querying that is serverless for analyzing data stored in IBM Cloud Object Storage. You can query your data right where it is stored - there are no ETL, databases or infrastructure to manage.

Open source data governance suite to manage data lakes and databases. Tokern is an easy-to-use toolkit for collecting, organizing and analysing metadata from data lakes. Runs as a command-line application for quick tasks. Run as a service to continuously collect metadata. Use reporting dashboards to analyze lineage, access control, and PII data. Or programmatically in Jupyter notebooks. Tokern is an open-source data governance suite for data lakes and databases. You can improve the ROI of your data, comply to regulations like HIPAA, CCPA, and GDPR, and protect your data from insider threats with confidence. Centralized metadata management for users, jobs, and datasets. Other data governance features are powered by this feature. Track column-level data lineage for Snowflake and AWS Redshift. You can build lineage using query history or ETL scripts. Interactive graphs and programming with APIs and SDKs allow you to explore lineage.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

You can automatically ingest your marketing data into any cloud file storage or data warehouse of your choice, such as BigQuery and Snowflake, Redshift and Azure SQL, AWS S3, AzureData Lake, Google Cloud Storage, and our fully managed ETL pipelines. Our hosted marketing data warehouse integrates all marketing data and provides ad insight, cross-channelattribution, content insights and competitor Insights. Our customer data platform enables a single view of the customer and their journey by allowing identity resolution across all data sources in real time. Electrik.AI, a cloud-based marketing software and full-service platform, is cloud-based. Electrik.AI's Google Analytics hit data extractor enriches the hit level data sent by the website or application to Google Analytics and periodically ships it to the desired destination database/data warehouse/file/data lake.

A single storage platform can eliminate data silos. Tiered storage and policy management can help you reduce costs. Azure Active Directory (Azure AD), and role-based access control(RBAC) can authenticate data. You can also help protect your data with advanced threat protection and encryption at rest. Flexible mechanisms provide protection for data access, encryption, network-level control, and more. Highly secure. A single storage platform that supports all the most popular analytics frameworks. Cost optimization through independent scaling of storage, compute, lifecycle management and object-level Tiering. With the Azure global infrastructure, you can meet any capacity requirement and manage data with ease. Large-scale analytics queries run at high performance.

RazorSQL is a SQL query tool, a database browser, SQL editor and database administration tool for Windows and macOS, Mac OS X and Linux. RazorSQL can connect to more than 40 databases and has been tested on them. View database objects including schemas, tables and columns, primary and secondary keys, views and indexes, procedures and functions, and more. Visual tools to create and modify, describe, execute, and delete database objects like tables, views and indexes, stored procedure, functions, triggers, etc. Multi-tabular display of queries, with options for filtering and sorting, searching, etc. You can import data from many formats, including Excel spreadsheets, fixed-width files, and delimited files. It comes with a robust relational database (HSQLDB), which is ready to use straight out of the box.

Mason learns with every query you type. Query, visualize, and share data seamlessly with your team. Our best-in-class AI assistant will help you generate SQL and refine the result. Mason will automatically complete your joins and recommends them to you. Say goodbye to endlessly scrolling through tables. Connect securely to BigQuery. Snowflake. PostgreSQL. and Redshift. You can easily collaborate with your team. Review query versions. View who made the changes. Restore to any time. Shared query library allows you to see all the queries your team has created. Visualize your queries in dashboards for each team to stay focused. Get feedback directly into your code. You won't waste time with this data tool. You can discover, navigate and perform any action anywhere. Keep up with the flow by using keyboard shortcuts. Discover the fastest way to visualize, query, and share. Our realtime sync delivers over 100m interactions worldwide.

The only fully-managed cloud data lakehouse that can ingest data from all of your sources in minutes, and support all of your query engines on a large scale. All for a fraction the cost. With the ease of fully managed pipelines, you can ingest data from databases and event streams in near-real-time. You can query your data using any engine and support all of your use cases, including BI, AI/ML, real-time analytics and AI/ML. Simple usage-based pricing allows you to cut your costs by up to 50% compared with cloud data warehouses and ETL software. With a fully-managed, highly optimized cloud service, you can deploy in minutes and without any engineering overhead. Unify all your data into a single source and eliminate the need for data to be copied between data lakes and warehouses. Apache Hudi, Apache Iceberg and Delta Lake all offer omnidirectional interoperability, allowing you to choose the best table format for your needs. Configure managed pipelines quickly for database CDC and stream ingestion.