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Data Analytics

Top Data Warehousing Tools

May 18, 2023 Leave a Comment

What is a Data Warehouse?

Data warehouse, also known as DWH is a system that is used for reporting and data analysis. It is considered to be the core of business intelligence (BI) as all the analytical sources revolve around the data warehouse. DWH is a central repository that stores current as well as historical data at one place. It contains integrated data from different sources and is used to prepare analytical reports which further get distributed to the knowledge workers in the enterprise. These reports help the organizations to understand/predict their sales patterns and design the marketing strategies accordingly.

How is Data processed in a Data Warehouse?

This can be well understood by taking the reference of the basic architecture of DWH.

All the operational sources place data into a staging area (staging tables/databases/schemas etc.) This data might need to pass through an operational data store that would cleanse the data. Data is cleansed in order to ensure the data quality before it is used for reporting.

Data warehouses that operate on typical Extract, Transform, Load (ETL) methodology use staging database, integration layers and access layers to carry out their functions. Staging databases store raw data coming from each data source and the integrating layer integrates it.

The integrated data is further arranged into hierarchical structures called dimensions. The cataloged data is made available to the managers and professionals for carrying out activities like data mining, market research, and decision support.

Top Pick of 10 Data Warehouse Tools

  1. Green Plum
  2. Oracle
  3. Amazon Redshift
  4. Teradata
  5. Oracle 12c
  6. Informatica
  7. IBM Infosphere
  8. Ab Initio Software
  9. ParAccel (acquired by Actian)
  10. Cloudera

1. Green Plum

Green Plum is open-source analytics, AI, and machine learning platform with a massively parallel architecture. Greenplum’s data analytics include data processing, textual information, graph data, time-series data, and geospatial data. Some of the computer languages supported are Java, Perl, Python, pgSQL, and R.

Key Features:

  • Scale interactive and batch analytics to petabyte-scale datasets without sacrificing query performance or throughput.
  • Greater software control, less vendor lock-in, and more open input into product direction
  • By merging analytic and operational functions, such as streaming ingestion, in a single, scale-out environment, data silos are reduced.

2. Oracle

Oracle data warehouse system is a group of data that is considered as a single entity. This database’s aim is to store and retrieve connected information. It enables the server to manage large volumes of data reliably, allowing several people to access the very same data.

Key Features:

  • Distributes data across drives in the same way to provide consistent performance.
  • Single-instance and real-world application clusters are supported.
  • Provides real-world application testing
  • Any Private Cloud and Oracle’s Public Cloud share the same architecture.
  • Large data requires a high-speed connection.
  • Compatible with both UNIX/Linux and Windows systems.
  • It has virtualization support.
  • Connecting to a remote database, table, or view is possible.

3. Amazon RedShift

Amazon Redshift is an excellent data warehouse product which is a very critical part of Amazon Web Services – a very famous cloud computing platform. Redshift is a fast, well-managed data warehouse that analyses data using the existing standard SQL and BI tools. It is a simple and cost-effective tool that allows running complex analytical queries using smart features of query optimization. It handles analytics workload pertaining to big data sets by utilizing columnar storage on high-performance disks and massively parallel processing concepts. One of its very powerful features is the Redshift spectrum, that allows the user to run queries against unstructured data directly in Amazon S3. It eliminates the need for loading and transformation. It automatically scales query computing capacity depending on data. Hence the queries run fast.

Features:

  • No Up-Front Costs for its installation
  • It allows automating most of the common administrative tasks to monitor, manage, and scale your data warehouse
  • Possible to change the number or type of nodes
  • Helps to enhance the reliability of the data warehouse cluster
  • Every data center is fully equipped with climate control
  • Continuously monitors the health of the cluster. It automatically re-replicates data from failed drives and replaces nodes when needed

4. Teradata:

Teradata is another market leader when it comes to database services and products. It is an internationally renowned company with its headquarters in Ohio. Most of the competitive enterprise organizations use Teradata DWH for insights, analytics & decision making. Teradata DWH is a relational database management system marketed by Teradata organization. It has two divisions i.e. data analytics & marketing applications. It works on the concept of parallel processing and allows users to analyze data in a simple yet efficient manner. An interesting feature of this data warehouse is its data segregation into hot & cold data. Here cold data refers to less frequently used data and this is the tool in the market these days.

Key Features:

  • A 360-degree perspective of your complete business, which is combined from all data sources, provides richer insights.
  • You may achieve the performance of in-memory databases without the expense by automatically storing the most frequently used data in memory.
  • Mission-critical availability and performance.

5. Oracle

Oracle is a well-established name in the data warehousing platform that was built for providing business insights and analytics to the users. Oracle 12c is a standard when it comes to scalability, high performance, and optimization in data warehousing. It targets at increasing the operational efficiency and thereby optimizing the end-user experience.

Its key features can be tabulated as:

  • Advanced analytics and enhanced data sets.
  • Increased innovation and industry-specific insights.
  • The maximum big data value.
  • Profitability
  • Extreme Performance & consolidation.

6. Informatica

Informatica is a data integration and management system developed by Informatica Corporation for gaining business insights. The repository saves the metadata information. Metadata information is the information contained in the destination systems, source systems, and transformations.

Key Features:

  • With ease, create, implement, and manage complicated APIs. Any application can connect and combine your data.
  • Deliver dependable, managed data to empower your analytics, enhance customer experience, and speed cloud modernization.
  • You can ingest, integrate, and cleanse your data with the market-leading, cloud-native ETL and ELT solution from the ETL pioneer.

7. IBM Infosphere

IBM Infosphere is an excellent ETL tool which uses graphical notations to execute data integration activities. It provides all the major building blocks of data integration & data warehousing along with data management and governance. The building foundation of this warehousing architecture is a Hybrid Data Warehouse (HDW) and Logical Data Warehouse (LDW). Multiple data warehousing technologies are comprised of a hybrid data warehouse to ensure that the right workload is handled on the right platform. It helps in proactive decision making and streamlining the processes. It reduces cost and is a very effective tool in terms of business agility. This tool helps in delivering intensive projects by providing reliability, scalability, and improved performance. It ensures the delivery of trusted information to the end-users.

8. Ab Initio Software

Ab Initio company holds a specialty in high-volume data processing and integration. Being launched in 1995, Ab Initio provides user-friendly data warehousing products for parallel data processing applications. It aims at helping organizations to perform fourth-generation data analysis activities, data manipulation, batch processing, and quantitative and qualitative data processing.
It is a GUI-based software that targets at easing off the extract, transform, and load tasks. Ab Initio software is a licensed product as the company prefers to maintain a high level of privacy regarding its products. People working on this product operate under an agreement of non-disclosure, called NDA (Non-disclosure Agreement) which prevents them from disclosing Ab Initio technical information publically.

Features:

  • Metadata management
  • Business and Process Metadata management
  • Ability to run, debug Ab Initio jobs and trace execution logs
  • Manage and run graphs and control the ETL processes
  • Components can execute simultaneously on various branches of a graph
  • Supports cloud data warehouses include Snowflake, Redshift, Synapse, RDS Aurora, BigQuery, AWS, Google Cloud, Microsoft Azure and Oracle Cloud

9. ParAccel (acquired by Actian)

ParAccel is a California-based software organization that deals in the data warehousing and database management industry. ParAccel was acquired by Actian in 2013

It provides DBMS software to organizations across all sectors. Two mainly offered products by the company include Maverick & Amigo. Maverick is a standalone datastore itself, however, Amigo is designed to optimize query processing speed that is generally redirected to an existing database.

Amigo was later on discarded by ParAccel and Maverick was promoted. Maverick gradually evolved as a ParAccel database that works on shared-nothing architecture and supports columnar orientation.

10. Cloudera

Cloudera is the first enterprise data cloud or multi-functional analytics platform to break down silos and expedite the generation of data-driven insights in the industry. It adds uniform security, governance, and metadata to shared data instances.

Key Features:

  • Without engaging the IT department, quickly change data, generate new reports and tasks, and access interactive dashboards.
  • Eliminate the inefficiencies of data silos by combining data marts into a Climbable analytics platform to meet company goals.
  • Construct and implement AI solutions at scale while staying within a budget.
Rajesh Kumar
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