In 2022, AI and big data analytics will be a watershed year as more companies expect concrete results. There will be 149 zettabytes of global data generated, copied, consumed, and captured by 2024. Customers and organizations worldwide produce vast amounts of data every minute, making big data analytics an incredibly compelling prospect.

By using custom-built big data tools, companies can put their data to work, uncover new opportunities, and create new business models. In contrast to traditional big data technologies, these solutions go one step further and provide contextual meaning to the raw data. With big data analytics tools, businesses can gain a more holistic view.

Here are the top business intelligence software resolutions for IT.

1. Apache Hadoop


In 2005, Doug Cutting and Mike Cafarella created Hadoop. Hadoop, Java-based Apache, is a software framework for large-scale data storage and processing commodity hardware clusters. Hadoop implements Google’s MapReduce, an application programming model featuring two fundamental operations for data processing: map and reduce. 

Using parallel computing divides large data sets into smaller workloads that can run in parallel across nodes in a computing cluster. As a framework, Hadoop modules are designed to automatically handle hardware failures (of individual machines or racks of machines) in software.

Hadoop operates in a distributed environment. It is scalable, unlike traditional data storage systems. Consequently, early data lakes were Hadoop-based. Complex drive-based data storage platforms such as Hadoop Distributed File System (HDFS) are fundamentally resilient. Hadoop replicates data stored on any cluster node to prepare for possible hardware or software failures. Hadoop allows you to store data in any format, including unstructured and semi-structured formats. With Hadoop, businesses can tap into new data sources and harness different types of data.

2. Xplenty


An ETL platform for integrating information from various databases into a wide variety of business applications, Xplenty helps companies read, process, and prepare data. The solution targets the retail, hospitality, and advertising industries. It enables businesses to connect immediately to big data stores and manage processes such as scheduling tasks and monitoring dataflows.

As part of Xplenty’s functionality, you can handle metadata management, extraction, transformation, loading (ETL), data preparation, pipeline design, job progress tracking, and activity monitoring. More than 120 integrations with SaaS platforms, databases, and business intelligence software tools are available through Xplenty. In addition to Amazon Redshift, Google BigQuery, and Snowflake data warehouses, the software supports Amazon S3 data lakes, along with file and database storage solutions. The company can build new data sources or destinations upon customer request, but no one outside the company can do so. Besides its dashboard, the platform comes with an API, allowing users to customize it as per their business needs.

3. Apache Cassandra


The Apache Cassandra distributed database is the only NoSQL database that provides always-on availability, lightning-fast read-write performance, and unbounded scalability. There are multiple Cassandra data centers, enabling easy data distribution wherever and whenever needed. A distributed database management system can handle large amounts of data in multiple data centers and the cloud. This feature provides users with blazing-fast write speeds, and large data volumes do not hamper the speed and accuracy.

CQL (Cassandra Query Language) is the query language used by Cassandra. Communicating with Cassandra is accomplished with this declarative language.

A NoSQL database is one of the most efficient and widely used systems. Unlike relational databases, it doesn’t have a single point of failure. It is crucial for businesses that cannot risk losing data or crashing their systems. Eliminating a single point of failure allows for maximum security and availability.

Originally, Cassandra was developed by Facebook for their inbox search feature. Initially developed by Facebook, Cassandra was integrated into the Apache Incubator in 2009. This has resulted in it being a top-level Apache project since early 2010. At present, it is a crucial part of the Apache Software Foundation.

4. Cloudera


Cloudera, a leading provider of structured, flexible, and scalable big data platforms, has enabled sophisticated, extensive data analysis using Apache Hadoop in any setting for more than a decade.

Engineers from Facebook, Google, Oracle, and Yahoo teamed up to create Cloudera in 2008. The idea of Cloudera was borne out of the need to develop a product for the public to harness Hadoop distribution software’s power.

The advanced data processing capabilities of Cloudera go beyond just data storage and archiving. The enhanced features of Cloudera enable users to analyze data in real-time and track and secure it across a variety of environments. Users can trace the origin of their data by using Cloudera’s comprehensive audits and lineage tracing tools. 

During the rise in Hadoop use, an ecosystem of data processing, analysis, and management programs has emerged around Hadoop’s core. Yet a steep learning curve has made it necessary to integrate platforms and other tools to reduce Hadoop’s complexity for end-users.

Cloudera has become the industry leader in building hybrid platforms for accessing Hadoop’s vast amount of storage, governance, and analysis opportunities using its open-source Hadoop and proprietary software. Cloudera’s products easily bridge the gap between Hadoop and SQL and NoSQL databases. It helps avoid data lakes becoming backwaters by decreasing the risk of data being dumped, thus preventing Hadoop from becoming obsolete. 

5. QlikView


The QlikView platform offers data integration, conversational data analytics, and the transformation of raw data into knowledge bases. QlikView simplifies making quick decisions based on readily available information, allowing users to create ad hoc queries. Using the associative model of the software, you can search for answers in virtually any direction.

Next-generation in-memory architecture is the key to QlikView’s associative search engine. BI tools that are fast, disk-based, and query-based are eclipsing traditional tools that offer little more than static, pre-packaged data. The QlikView interface loads all your data in memory and lets you perform real-time associative searches and analysis with just a few clicks.

In QlikView, analytic calculations are built both on the back and front end. The BI solution lets developers build dashboards, analyses, and reports in one place rather than using separate tools. The resulting QlikView application provides powerful associative search and data visualization capabilities, enabling users to visualize the underlying data in the way they find most meaningful.

6. Talend


Talend is another open-source software tool for automating extensive data integration through graphical tools. This allows organizations to develop an environment compatible with Apache Hadoop, Spark, and NoSQL data stores for on-premises or cloud jobs.

Its architecture is open and scalable, providing faster business responses. Talend ensures speedier development and deployment of data integration jobs than coding by hand.

All your data can be integrated with other data warehouses and synchronized between systems. By integrating data from different sources, you can provide your users with a unified view of these data. Users are empowered with simple, self-service data preparation while managing various ETL processes.

Talend’s main product is Talend Open Studio, used for system integration and Enterprise Integration, Transformation, and Loading for Business Intelligence and Data Warehousing. Talend’s technology and business model reflect a completely new vision for business intelligence software.

7. Zoho Analytics


Zoho Analytics is a BI and data analytics software platform designed to allow users to visually analyze data, create visualizations, and further understand raw data. For greater insight, it enables users to generate dynamic, highly customizable, and actionable reports and allows seamless integration with multiple platforms. The dashboards’ flexibility and customization enhance a user’s experience.

A few clicks and minimal setup are all it takes to generate actionable intelligence from analytics. Zoho Analytics delivers incisive reports for tracking trends and live dashboards for monitoring changes through its intelligent, secure way of aggregating and organizing data.

The Zoho Analytics API can pull data from several standard tabular formats, including CSV, XLS, and HTML, or fetch records from an existing database.

Zoho Analytics will create a new Workspace for your data once you connect it. Dashboards, reports, and other analytics are stored in workspaces.

8. MongoDB


A highly scalable, robust, accessible, and open-source NoSQL database, MongoDB was developed in 2009. Today, Forbes, Facebook, Google, IBM, Twitter, and many others use it as the backend data store of their organization. 

The MongoDB database system is non-relational. Databases fall into two categories: SQL (relational) and NoSQL (non-relational). A relational database stores information in rows and columns. Many companies use relational databases like Microsoft SQL Server and Oracle.

With MongoDB, documents can include sub-documents in hierarchical structures, making them expressive and flexible. With MongoDB, objects can be mapped in and out of any language, making implementation and maintenance easier.

An application has the authority and responsibility to interpret properties in a collection’s documents. The native aggregation feature allows users to extract and transform data from databases. Using regular expressions, ranges, or attribute values, users can selectively index portions of documents, allowing them to query for some portions or have as many properties per object as they need. Alternatively, data can be exported to other data sources or loaded into a new format.

9. GoodData


A data analytics platform that can create, deliver, and manage analytics at any scale. GoodData powers modern business intelligence software. The platform offers self-service analytics, low code/no code interfaces, embeddable data visualizations, and application integration. A robust end-to-end pipeline is also included, including data ingestion, preparation, and transformation.

The composable architecture allows GoodData to integrate seamlessly with existing infrastructure and create analytics solutions that are easy to deploy for employees, customers, or partners. A key component of GoodData’s analytics consulting platform is the headless BI engine. It runs on containerized microservices, is managed as code, and separates analytical backends from consumption layers. The engine’s semantic layer enables applications, data platforms, or visualization tools to interact via APIs and standard interfaces and access consistent analytics in real-time, anywhere.

10. Alteryx


The Alteryx data cleansing software is also used for data mining, ETL, spatial analysis, machine learning, and other BI-related activities. It can be used for relatively simple operations such as joining different data sources and pre-processing and more advanced operations such as predictive modeling, time series, and simulations.

Alteryx’s analytics approach differs from many others. Platforms and services are solely intended to develop new data partnerships between analytics, IT, and companies of the same kind. You can tackle complex analytical business problems with Alteryx allowing you to drive great business results.

How do I get an edge on my competition with Ascend Analytics Consulting Firm?

Several factors influence your decision on a data platform. Taking advantage of your digital transformation will enable you to yield actionable insights and deliver tremendous value. In addition to descriptive, diagnostic, and prescriptive analytics, Ascend Analytics consulting and service providers can offer you advanced analytics to boost your organization’s performance. With the help of data science service providers, the company can drive operational efficiency and profit margins. Furthermore, their data analysis experts can help your organization devise and execute business-critical solutions based on digital data. The analytics consulting company specializes in data and AI for business intelligence across industries. Making them uniquely qualified to help your business, whether to enhance customer experience, reduce costs, or shape products and services.

A combination of the following platforms will provide you with a data-centric solution that resonates with your needs!

  • Artificial intelligence
  • Machine Learning
  • Data Warehousing
  • Data Analysis
  • Predictive Analytics
  • Predictive Modeling
  • Data Mining
  • Data Visualization

Work ethics at the company follow the following loop of rituals to achieve the desired set of customer demands:

  • Reframe the issue
  • Gather raw statistics
  • Analyze the data
  • Conduct an in-depth analysis
  • Visualize the results of your analysis

Frequently Asked Questions

  • Which data analytics tools do Ascend Analytics use?

Data is the lynchpin of our team of specialists’ ability to develop end-to-end business intelligence solutions. Our primary working tools are Google Big Query, MySQL, Power BI, Tableau, etc. For detailed information, check out our website:

  • Which tools are best suited for me?

We offer descriptive, diagnostic, prescriptive, and advanced analytics following your needs. We have stack fluency and platform knowledge expertise to bring your company vision to life. Our solutions are technology-based, backed by end-to-end implementations and strategic advice. 

  • How does Ascend Analytics call into action extensive data analysis and visualization? 

Our BI service providers analyze and synthesize complex data for proper use by forecasting, optimizing, and crafting a well-defined strategy. In our typical workflow, we include the following: 

  • Importing and Cleansing data
  • Data Mining
  • Viewing and Visualizing big data
  • Achieving casual usability
  • Dissemination
  • Evaluation


Forward-looking companies use some of the most recent big data technologies and data analytics to drive their growth. Essentially, these resources are the on-demand computing resources, particularly those used to handle and store data. For addressing all your data eccentricities, you’ll need a platform that can integrate multiple features and functionalities. Under Ascend Analytics’ modern approach, various analytical disciplines can be combined into one platform that can run on-premises, in the cloud, or a hybrid model. The next best step to your business’s revolution!

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