Data Analysis Tools: Features and Top Software
Since 1989, ScienceSoft has been rendering data analytics services to assist companies with choosing and implementing optimal data analytics software.
Data Analytics Software: the Essence
Data analytics software enables the end-to-end analytics process by retrieving data from one or more internal and external data sources, integrating it into a centralized structured repository (a data warehouse), and analyzing and visualizing key insights for business users.
Key Features of a Data Analytics Solution
Data integration and management
- Data collection from internal (e.g., CRM, ERP, accounting software, website) and external (e.g., social media) data sources.
- Different data extraction and load modes (scheduled bulk/batch, streaming/near-real-time.)
- Data transformation of varying complexity (data-type conversion, summarization, etc.)
- Data model creation and maintenance.
- Metadata management (metadata discovery and acquisition.)
- Storing historical, subject-oriented data in a centralized structured repository (DWH).
- Storing structured, semi-structured and unstructured data at any scale (data lake).
- Storing data oriented to a specific business line or team (data mart.)
- Metadata storage.
- Online analytical processing (OLAP).
- Heterogeneous data handling (structured, semi-structured, unstructured data).
- Different data analytics modes (e.g., batch, streaming analytics.)
- Descriptive and diagnostic analytics.
- Geospatial advanced analytics.
- Augmented analytics for automatically generating analytics insights.
- Data mining (both structured and unstructured data.)
- Machine learning (including deep learning) models creation for predictive analytics and forecasting.
Reporting and visualization
- Interactive data exploration and discovery.
- Interactive dashboarding.
- Pre-built and custom visual elements.
- Collaborative visualization.
- Scheduled and ad-hoc reporting.
- Mobile reporting.
- Data encryption.
- Securing data access with user authentication and authorization.
- Fine-grained access control (row- and column-level).
- Report- and workspace-level security.
Top 6 Data Analytics Tools for Comparison
Best for: self-service business analytics
BI and data visualization tools.
A recognized leader (Gartner, Forrester, IDC) among self-service data analysis software. Power BI comprises a set of products – Power BI Desktop, Power BI service, Power BI Mobile, Power BI Report Server, Power BI Embedded. 100+ native data source connectors, multi-language support, AI-driven data preparation and analysis (including big data), pre-built and custom visuals, interactive visualization and dashboarding, intuitive interface, enhanced security.
DEMO: Watch our Power BI demo.
- Power BI Desktop – free.
- Power BI Pro – $9.99/user/month.
- Power BI Premium – $4,995/dedicated resources/month.
Best for: cloud ETL/ELT
Data integration tools.
An Azure-based solution for ingesting, preparing and transforming data at scale. 90+ built-in maintenance-free connectors, ETL/ELT code-free processes supported by Apache Spark, easy migration of SQL Server Integration Services (SSIS) workloads, on-demand scaling, pay-as-you-go pricing model.
- Data pipelines: pipelines orchestration – $1/1,000 activity runs/month; data movement activity – $0.25/Data Integration Unit/hour; pipeline activity - $0.25/hour; external pipeline activity – $0.10/hour.
- Data flow execution and debugging: $0.193 - $0.325/virtual core-hour (depending on cluster type).
- Data Factory operations: read/Write – $0.50/50,000 modified/referenced entities; monitoring – $0.25/50,000 run records retrieved.
Best for: Big data analysis
Big data tools.
An Azure service for big data analytics with open-source frameworks and languages such as Hadoop, Apache Spark, Apache Hive, Apache Kafka, Apache Storm, R, and more. Integrations with BI tools (Power BI, Excel, SQL Server Reporting Services, etc.) and Azure data storage and analytics services (Data Lake Storage, Azure Blob Storage, Azure Cosmos DB, Data Factory, Azure Synapse Analytics, etc.). Data encryption, multi-user support, role-based access control.
- Node-hour – $0.06/hour - $5.416/hour.
- Hadoop, Spark, Interactive Query, Kafka, Storm, HBase – Node-hour + $0/core-hour.
- HDInsight Machine Learning Services – Node-hour + $0.016/core-hour.
- Enterprise Security Package –Node-hour + $0.01/core-hour.
Best for: agile ML
Machine learning (ML) tools.
An enterprise-grade machine learning service for building, training, and deploying ML models fast. Supports open-source frameworks and languages for ML development (MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R). Automated ML development with built-in feature engineering, algorithm selection, hyperparameter sweeping, etc. Streamlined ML development life cycle with MLOps capabilities. Data encryption, built-in granular role-based access control, built-in identity authentication.
Generally available (payment for the Azure resources consumed, for example, compute and storage costs) vCPU pricing $0.042- $26.688/hour.
Best for: big data analysis
Big data tools.
A big data platform for processing and analyzing large data volumes with open-source tools (Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto). Easy provisioning, scaling, and reconfiguring of clusters, manual/automatic capacity up- and downscaling. Secure and low-cost data storage enabled by integration with Amazon EC2 Spot, Amazon EC2 Reserved Instance, and Amazon S3.
From $0.011/hour to $0.27/hour ($94/year to $2367/year).
Best for: cloud ML at scale
Machine learning (ML) tools.
A leader in Gartner’s Magic Quadrant for Cloud AI Developer Services, Amazon SageMaker is a fully managed service for the entire machine learning workflow. Supervised and unsupervised reinforcement learning algorithms. Optimized for working with major ML frameworks (TensorFlow, Apache MXNet, PyTorch, Chainer, Keras, etc.). Automatic building, training and tuning of ML models with full visibility and control with Amazon SageMaker Autopilot. High performance and on-demand pricing.
- Building – ML compute instance: from $0.0582 to $28.152 /hour/instance + storage: GB-month or Amazon Elastic File System (EFS) storage.
- Training – ML compute instance: from $0.134 to $35.894 /hour/instance + storage: GB-month.
- Real-Time Inference – ML compute instance: from $0.065 to $28.152/hour/instance + storage: GB-month + data processing - $0.016/GB.
- Batch Transform – ML compute instance: from $0.134 to $28.152 /hour/instance.
Data Analytics Implementation with ScienceSoft
At ScienceSoft, we offer 34+ years of experience in data analytics consulting and implementation to establish your data analytics solution with minimal expenses and maximum ROI.
Data analytics software consulting
We help you analyze your data analytics objectives and define:
- Requirements to a data analytics solution.
- A data analytics solution’s architecture.
- An optimal technology stack.
- Data quality and security techniques.
- Implementation and user adoption strategies.
Data analytics implementation
Our team of data analysts:
- Helps you choose a data analytics technology stack.
- Defines data analytics software configurations.
- Delivers PoC for complex projects.
- Integrates data analytics software with the required data source systems, sets up ETL processes, builds OLAP cubes, etc.
- Runs QA (validating the data analytics solution).
- Provides user training and support programs, if required.
ScienceSoft is a global IT consulting and IT service vendor headquartered in McKinney, TX, US. Since 1989, we have been providing data analytics consulting and related services to help companies select optimal data enterprise analytics software and leverage its capabilities to ensure maximum ROI out of data analytics investments. Being ISO 9001 and ISO 27001-certified, we rely on a mature quality management system and guarantee cooperation with us does not pose any risks to our customers’ data security.