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Big Data Leaders: GridGain, Lattice Engine, Origami Logic, Rainstor, and Sumo Logic

Big Data Leaders: GridGain, Lattice Engine, Origami Logic, Rainstor, and Sumo Logic

Data that is uncorrelated and does not have a pre-defined data model and is not organized in a pre-defined manner requires special handling and analytics techniques. The common industry term, Big Data, represents unstructured data sets that are large, complex, and prohibitively difficult to process using traditional management tools.

In previous Leading Big Data Companies reports, Mind Commerce has covered larger, more well-known and evaluated Big Data companies. In this edition of Big Data Leader research, Mind Commerce evaluates smaller, less-known companies that have innovative solutions and great promise to solve the many challenges presented by huge datasets and generate information/insights from them with minimal delay time (sometimes in real-time).

Companies evaluated in this report* includes:

Lattice Engine
Origami Logic
Sumo Logic

For each company evaluated in this report we include the following:

Company Overview
Offering Analysis
Strategies and Plans
Mergers and Acquisitions
Partnerships and Alliances
Key Contract Wins Assessment
Analysis and Conclusions

*Note: Mind Commerce plans to evaluate additional companies in Big Data (look for similar reports).

All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Big Data and analytics companies
Data as a Service (DaaS) companies
Cloud-based service providers of all types
Data processing and management companies
Application Programmer Interface (API) companies
Public investment organizations including investment banks
Private investment including hedge funds and private equity

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.

1. Big Data Leadership
2. Lattice Engine
2.1. Overview
2.2. Big Data Leadership
2.3. Strategies & Plans
2.4. Partnerships & Alliances
2.5. Key Contract Wins
2.6. Analysis And Conclusion
3. Rainstor
3.1. Overview
3.2. Big Data Leadership
3.3. Strategies And Plans
3.4. Partnerships & Alliances
3.5. Key Contract Wins
3.6. Analysis And Conclusion
4. Sumo Logic
4.1. Overview
4.2. Big Data Leadership
4.3. Strategies And Plans
4.4. Partnerships & Alliances
4.5. Key Contract Wins
4.6. Analysis And Conclusion
5. Origami Logic
5.1. Overview
5.2. Big Data Leadership
5.3. Strategies And Plans
5.4. Key Contract Wins
5.5. Analysis And Conclusion
6. Gridgain
6.1. Overview
6.2. Big Data Leadership
6.3. Strategies And Plans
6.4. Partnerships & Alliances
6.5. Key Contract Wins
6.6. Analysis And Conclusion
Figure 1: Rainstor Architecture Overview
Figure 2: Rainstor Solution Offerings
Figure 3: Stages Involved In Data Collection By Sumo Logic
Figure 4: Gridgain - Products And Solutions Offerings
Figure 5: Gridgain In-memory Data Fabric Architecture
Table 1: Lattice Engine - Recent Partnerships & Alliances
Table 2: Lattice Engine - Contract Wins, 2012-2014
Table 3: Rainstor – Recent Partnerships & Alliances
Table 4: Sumo Logic Solution Offerings
Table 5: Sumo Logic –recent Partnerships & Alliances

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